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RPA Revolution in the Healthcare Industry During COVID-19
Over the last year, the evolution in Robotic Process Automation (RPA) has been staggering. The automation it brings to applications has yielded efficiency, reduced operating costs, and decreased the time of research, development, and production. Industries have already integrated RPA into their workflow and are profoundly transforming into an intelligent automated industry with minimum human intervention, calling this the fourth industrial revolution. In this race of transformation, the healthcare industry is quite ahead of many other industries. It stood the test of time when COVID-19 was spreading rapidly and was also resilient against all odds. The system did experience an unprecedented crisis that depicted its weakness, fragility, and unpreparedness. The healthcare system was forced to adapt to a new paradigm. And though there was the loss of life and economy, we learned to evolve as a community to tackle this crisis. This chapter sheds light on the role of RPA and covers how these technologies can assist healthcare workers in their day-to-today activities, reviewing what the fourth industrial revolution would look like in the healthcare sector. The intelligent, automated system would provide a seamless experience of gathering information by various means, processing, and assisting healthcare workers to deliver quality treatment. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Detecting Infectious Disease Based on Social Media Data Using BERT Model
Seasonal diseases are those diseases that are widespread during a particular time of the year including monsoons, winter etc. In the absence of preventative measures, the human race remains vulnerable to the hazardous effects of seasonal diseases following regular patterns of increased inci- dence and transmission which remains a global concern. Dengue, Influenza, etc. are such types of diseases where every year many people get affected globally. The primary focus of this research paper is to understand the opinion of people regarding the seasonal diseases. The research paper covers sentiment analysis on textual data from social media where people have vocalized their sentiments or thinking regarding seasonal diseases and seasonal infectious diseases. Influenza, Dengue, Malaria, Japanese Encephalitis, and Chikungunya are the seasonal diseases that have been covered in this research paper. To achieve this, the language model Bidirectional Encoder Representations from Transformers (BERT) was used to verify the sentiments about the seasonal diseases. The result of the investigation hold the potential to significantly enhance our comprehension of societal sentiments, discerning between states of tranquility and concern among individuals. The outcome of the study will help healthcare department to plan the necessary actions. 2024 IEEE. -
A critical review of determinants of financial innovation in global perspective
Financial innovation is the widely accepted process across the globe. 'What forces drive the financial innovation?' is the research question since long. Many studies were conducted in the past to answer and each study identified some or other factors that prominently driving financial innovation landscape in their respective economy. The present study critically review existing Literatures to suggest a comprehensive list of determinants. The study uses descriptive research design. A sample of 54 literatures focusing on financial innovation and it's determinants during the time period 1983 to 2018 is included in the study. Further, content analysis and descriptive statistics are used to explore the determinants. The study identified 23 different determinants of financial innovation and classify those under two bases. First, on the basis of influencing power and second on the basis of nature of the determinant. The study found that technological development, competition, firm size and regulations are the major sources of financial innovation from different categories. The study also raised the research agenda to study determinants of financial innovation in Asian context, as there are scanty literature covering Asian economies. 2021 Elsevier Ltd. All rights reserved. -
Classification of Multiclass DDOS Attack Detection Using Bayesian Weighted Random Forest Optimized With Gazelle Optimization Algorithm
The increase in Distributed Denial of Service (DDoS) attacks poses a considerable threat to the security and stability of the current network, especially in Internet of Things (IoT) and cloud environments. Traditional detection methods often struggle with the inability to achieve a balance between detection accuracy and computational efficiency. In this manuscript, the Classification of Multiclass DDOS Attack Detection using Bayesian Weighted Random Forest Optimized with Gazelle Optimization Algorithm (DDOS-AD-BWRF-GOA) is proposed. First, the raw data is gathered from the CICDDoS2019 dataset. Then, input data are preprocessed utilizing Adaptive Bitonic Filtering for normalizing the values. The preprocessed data are fed to the Improved Feed Forward Long Short-Term Memory technique for selecting features that increase the model's execution time. The selected features are supplied to the Bayesian Weighted Random Forest (BWRF), which classifies the multiclass DDOS attack. In general, Bayesian Weighted Random Forest does not adopt any optimization methods to define optimal parameters to guarantee exact DDOS identification. Hence, GOA is proposed to optimize the Bayesian Weighted Random Forest classifier. The proposed method is implemented in MATLAB. The performance metrics, such as Accuracy, Precision, Recall, F1-score, Specificity, Error rate, and Computational time are evaluated. The proposed method attains 15.34%, 24.1%, and 18.9% higher accuracy and 12.4%, 18.24%, and 22.6% higher precision when analyzed with existing techniques: Hybrid deep learning method for DDOS detection and classification (HDL-DDOS-DC), Edge-HetIoT Defense against DDoS attack utilizing learning techniques (EHD-DDOS-LT), and Digital twin-enabled intelligent DDOS detection for autonomous core networks (DTI-DDOS-ACN), respectively. 2025 John Wiley & Sons Ltd. -
A Search for X-Ray/UV Correlation in the Reflection-dominated Seyfert 1 Galaxy Markarian 1044
Correlated variability between coronal X-rays and disk optical/UV photons provides a very useful diagnostic of the interplay between the different regions around an active galactic nucleus (AGN) and how they interact. AGNs that reveal strong X-ray reflection in their spectra should normally exhibit optical/UV to X-ray correlation consistent with reprocessingwhereas the optical/UV emission lags behind the X-rays. While such correlated delay has been seen in some sources, it has been absent in others. Mrk 1044 is one such source that has been known to reveal strong X-ray reflection in its spectra. In our analysis of three long XMM-Newton and several Swift observations of the source, we found no strong evidence for correlation between its UV and X-ray lightcurves both on short and long timescales. Among other plausible causes for the nondetection, we posit that higher X-ray variability rather than UV and strong general relativistic effects close to the black hole may also be responsible. We also present results from the spectral analysis based on XMM-Newton and NuSTAR observations, which show the strong soft X-ray excess and iron K? line in the 0.3-50 keV spectrum that can be described by relativistic reflection. 2023. The Author(s). Published by the American Astronomical Society. -
Correlated variability of the reflection fraction with the X-ray flux and spectral index for Mrk 478
The X-ray spectrum of Mrk 478 is known to be dominated by a strong soft excess that can be described using relativistic blurred reflection. Using observations from XMM-Newton, AstroSat, and Swift, we show that for the long-term (?years) and intermediate-term (days to months) variability, the reflection fraction is anticorrelated with the flux and spectral index, which implies that the variability is due to the hard X-ray producing corona moving closer to and further from the black hole. Using flux-resolved spectroscopy of the XMM-Newton data, we show that the reflection fraction has the same behaviour with flux and index on short time-scales of hours. The results indicate that both the long- and short-term variability of the source is determined by the same physical mechanism of strong gravitational light bending causing enhanced reflection and low flux as the corona moves closer to the black hole. 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
The Impact of Computer-Mediated Communication on Relationships and Social Interactions
Computer-mediated communication (CMC) has profoundly changed how we express or connect in the modern world. Various virtual platforms, like Instagram, WhatsApp, and online games, have transformed how we communicate, and there is an overlap between the virtual and the physical world. This reflective study uses a comprehensive literature synthesis to examine the transforming nature of CMC on relationships and socialization patterns. The findings emphasize the importance of a holistic approach to understanding technology in interpersonal communication. Through this study, we attempt to mitigate the potential harms of excessive internet use through digital literacy, reflecting on online interactions and mindfulness in using the medium, especially for school-age children. The main takeaway from this reflective research is that when using technology for communication, one should practice equality and fairness across the board. Both the real and virtual worlds operate on the same principles of similarity and social exchange to create relationships, even though these theories are based on traditional offline relationships. 2024 Taylor & Francis Group, LLC. -
Deep Learning Enabled Parent Involvement and Its Influence on Student Academic Achievement Analysis
Studying the substantial effect that Deep Learning Enabled Parent Involvement (DLEPI) has on kid academic success. Using a made-up data set and a neural network model, we find that parents' level of involvement, as measured by the Parental Involvement Score (PIS), is positively correlated with their children's academic performance. DLEPI, driven by cutting-edge deep learning algorithms, equips parents with unique insights and suggestions regardless of where they live, therefore promoting educational equality and diversity. This study underlines the potential of technology to reduce performance inequalities and highlights its central role in increasing parental participation. Critical elements for future study include ethical issues, real-world validation, effect evaluations over time, and chances for personalization. This research lays the groundwork for reinventing education in a future where DLEPI improves student outcomes and offers a more inclusive and personalized educational environment. 2024 IEEE. -
Artificial Intelligence Influence on Leadership Styles in Human Resource Management for Employee Engagement
In this work, we investigate how the revolutionary effects of AI on leadership styles in the field of human resource management (HRM) have impacted employee motivation. To investigate the intricate relationship between AI adoption, HR management, and employee morale, we use a mixed-method approach, combining quantitative survey data with qualitative interview results. Both Leadership Style Change (LS-Change) and Employee Engagement (EE) show a statistically significant positive correlation with AI adoption. In the new AI-enabled HRM environment, HR executives are shifting their methods of leadership, adopting more flexible styles, giving workers more autonomy, and improving lines of communication. This research links theory and practice by providing actionable advice to HR managers and business owners. In order to further develop the topic of AI-enhanced HRM, future studies should investigate longitudinal dynamics, cross-industry variances, cultural and ethical issues, cutting-edge AI applications, and employee perspectives. 2024 IEEE. -
Implication of emotional labor, cognitive flexibility, and relational energy among cabin crew: A review
The primary aim of the civil aviation industry is to provide a secured and comfortable service to their customers and clients. This review concentrates on the cabin crew members, who are the frontline employees of the aviation industry and are salaried to smile. The objective of this review article is to analyze the variables of emotional labor, cognitive flexibility, and relational energy using the biopsychosocial model and identify organizational implications among cabin crew. Online databases such as EBSCOhost, JSTOR, Springerlink, and PubMed were used to gather articles for the review. The authors analyzed 17 articles from 2001 to 2016 and presented a comprehensive review. The review presented an integrative approach and suggested a hypothetical model that can prove to be a signitficant contribution to the avaition industry in particular and to research findings of aviation psychology. 2018 Indian Journal of Occupational and Environmental Medicine | Published by Wolters Kluwer-Medknow. -
A Hybrid Stacked Ensemble Model for Heart Disease Prediction
Cardiovascular Diseases (CVDs), especially heart attacks, are resulting in high rates of death worldwide, which highlights the need for early prediction systems. This paper deals with advanced ML and DL methods to predict heart attacks with a pre-processed clinical dataset. 6 models were used: a Hybrid Stacked Model combined with Logistic Regression, Random Forest, and XGBoost using a neural meta-learner; CNN with LSTM, BiGRU, and dense layers; an RNN with BiLSTM; and an XGBoost method using deep feature representations. Data preprocessing involved feature scaling and class balancing with the help of SMOTE. Model performance is being measured by Accuracy, Precision, Recall, and F1-Score. Hybrid Stacked Model had the highest accuracy (94.24%) and F1-score (94.12%), while CNN + LSTM had the best recall (95.96%), to reduce false negatives. XGBoost with deep features demonstrated competitive accuracy (91.22%) and transparency. These results point to the efficiency of hybrid and sequential deep learning models in cardiovascular risk prediction. In the future, research will be focused on real-time patient data integration, federated learning for privacy, and personalized health promotion using IoT-based monitoring. 2025 IEEE. -
The Impact of Computer-Mediated Communication on Relationships and Social Interactions
Computer-mediated communication (CMC) has profoundly changed how we express or connect in the modern world. Various virtual platforms, like Instagram, WhatsApp, and online games, have transformed how we communicate, and there is an overlap between the virtual and the physical world. This reflective study uses a comprehensive literature synthesis to examine the transforming nature of CMC on relationships and socialization patterns. The findings emphasize the importance of a holistic approach to understanding technology in interpersonal communication. Through this study, we attempt to mitigate the potential harms of excessive internet use through digital literacy, reflecting on online interactions and mindfulness in using the medium, especially for school-age children. The main takeaway from this reflective research is that when using technology for communication, one should practice equality and fairness across the board. Both the real and virtual worlds operate on the same principles of similarity and social exchange to create relationships, even though these theories are based on traditional offline relationships. 2024 Taylor & Francis Group, LLC. -
A Paradigmatic Shift: Telehealth Counselling's Expansion and Challenges in India
Background: This study provides a comprehensive analysis of the rapid expansion and transformative impact of telehealth counselling in India, a trend significantly propelled by the challenges posed by the COVID-19 pandemic. Methodology: This paper presents a perspective on the current telehealth landscape, synthesizing insights from an extensive literature review. The investigation integrates qualitative insights from health care practitioners and clients, allowing for a multifaceted understanding of the emerging obstacles linked to telehealth implementation. The synthesis is structured around several key concepts identified in the literature, including the efficacy of telehealth counselling services compared to traditional face-to-face interactions, the resilience of mental health services during crises, and the growing acceptance of digital modalities among patients. Additionally, it explores significant challenges such as disparities in technological access, the need for comprehensive regulatory frameworks, varying levels of patient receptivity, infrastructural limitations, and the readiness of health care professionals to adopt telehealth technologies. Results: By focusing on these areas, the paper elucidates the complex interplay of technical, regulatory, and cultural factors shaping the telehealth ecosystem in India. It advocates for urgent policy enhancements and the continuous integration of technology to effectively address these barriers. Discussion: This perspective underscores the potential for telehealth counselling to evolve into a permanent and essential component of India's mental health service delivery model, ultimately contributing to a more resilient and accessible health care system. Conclusion: The conclusions drawn emphasize the necessity for targeted policy interventions and the establishment of robust technological infrastructures to foster a more inclusive and effective telehealth environment, ensuring mental health services reach all segments of the population. 2025 John Wiley & Sons Ltd. -
The role of family structure in shaping psychological experiences of emerging adults: A mixed methods study
Background: Family structure plays a pivotal role in shaping individuals psychological development, particularly during emerging adulthood. Aim: In India, where joint and nuclear family systems coexist, understanding how these structures influence psychological variables such as conformity, loneliness, perceived self-efficacy, psychological distancing, and the need for affiliation is critical. Method: This study employed a mixed-methods approach to examine these variables among 470 emerging adults (298 females, 172 males) aged 18 to 25?years, recruited from urban and semi-urban areas in India. Quantitative data were collected using standardized tools, while qualitative insights were gathered through semi-structured interviews with 20 participants. Results: Quantitative results revealed that emerging adults from joint families reported significantly higher levels of self-efficacy compared to those from nuclear families (U?=?18,945, p?=?.03), while no significant differences were found in loneliness (U?=?25,140, p?=?.73) or conformity (U?=?20,735, p?=?.57). A weak negative correlation was found between loneliness and self-efficacy (rs?=??.20, p?<?.05), indicating that higher loneliness is associated with lower self-efficacy. Qualitative findings highlighted the role of family as a source of emotional security, with technology bridging emotional gaps across family types. Participants exhibited a present-focused planning mindset, emphasizing adaptability over rigid long-term goals. Both joint and nuclear family participants relied on familial and peer networks to fulfill affiliative needs, though the nature of these networks varied by family structure. Conclusion: The study concludes that while joint families were associated with higher self-efficacy, both family types provided emotional security and fulfilled affiliative needs in distinct ways. These findings underscore the importance of considering both structural and relational aspects of family dynamics in understanding young adults psychological well-being. Future research should explore these dynamics across different cultural contexts and age groups to identify universal and culture-specific patterns. The Author(s) 2025 -
TECHNOLOGY-ENABLED SOLUTIONS FOR INCLUSIVE WORKPLACE DESIGN TO SUPPORT TRANSGENDER EMPLOYMENT RIGHTS
The study fills the gap in the current understanding of available legal safeguards and the actual inclusion of transgender workers that, despite constitutional and legislative requirements in India, there is still no equal access to work-related facilities, computerized systems, and company policies. In order to fill this gap, the research will use a User-Centered Design (UCD) methodology, which involves the active involvement of transgender employees in all the phases of requirement collection, co-design, prototyping, implementation, and testing. The model includes adaptive digital platforms, workspace redesign, smart engine, other policy-promoting, and promotes inclusivity with the help of data analytics and feedback loops. Pilot testing and simulated data evaluated parameters, including participation by the user, ergonomic inclusivity, policy-response intelligent, job satisfaction, mental wellbeing, retention, and team functioning. Findings show significant gains compared to conventional HR models with a 95% increment in user participation, 4% increment of ergonomic inclusiveness, 65% increment of policy-response acumen, 4.5-point increment of job satisfaction, 8 % increment of mental wellbeing, 41-point increment of team collaboration and improvement in retention by 20% in three-years. Overall, these results indicate that UCD-based technology-enhanced systems have the potential to decrease the level of exclusion, improve psychological safety, and performance of organizations. This paper finds that sustainable transgender workplace equity must incorporate participatory feedback, inclusive design and technology-based solutions into the system of the organizations, developing scalable and evidence-based programs to enhance inclusion, wellbeing, satisfaction, and retention and creating a supportive and equitable workplace environment. 2025, Technical institute of Bijeljina. All rights reserved. -
Unlocking Access: Technology as a Tool for Transgender Economic Justice
The Transgender community across the world face discrimination from all walks of life. The transgender, even if educated and skilled, faces multiple hurdles during the employment process and within the workplace once employed. The challenges have even increased with COVID-19 and mass layoffs affecting employees across the globe. Transgender people have to be empowered to face the challenges of employment processes and workplace challenges as and when they arise. With the advancement of digital technologies, the Fourth Industrial Revolution can be leveraged for training, empowerment, awareness, network building, grievance redressal and sustainability for the transgender community. Digital technologies leverage the community to collaborate and be vocal about their needs and rights at a global level. Digital technology also helps the transgender community reach the proper forum to implement the required program for the benefit of the community. The decentralized and anonymous system for recording data and reporting incidents will also be a helpful, transparent process without being subject to unnecessary scrutiny. The technology-enabled system and practices will also help the companies and institutions to ascertain appropriate and efficient methods for creating gender gender-neutral environment devoid of any discrimination. Recently, there was a significant rise in concerns about employment practices for the LGBTQ community around the globe during the pandemic, such as lockdown protocols affecting employment conditions, vaccine access, support from employers, severance pay, mental health, etc. The International ecosystem has seen minimal regulatory discussion, and individual countries and companies are now implementing schemes to address the issue faced by transgender individuals in the employment and work ecosystem through the infusion of technologies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
OrthoTrace - Fracture Detection from X-Ray Images Using YOLOv5
Rapid and correct diagnosis of bone fractures is important for appropriate and efficient management in clinical practice; however, traditional X-ray interpretation frequently involves diagnostic error, particularly in low resource environments. This study presents OrthoTrace, a YOLOv5-based deep learning system designed for real-time and lightweight fracture detection. The model was trained on a curated set of annotated X-ray images from the publicly available YOLOv5 dataset, which includes a range of upper and lower limb fractures across diverse anatomical regions regions ( 3,500 - 4,000 images; 70:20:10 train/validation/test split) and evaluated using accuracy, mean Average Precision (mAP), precision, recall, and F1-score. On the held-out test set, OrthoTrace achieved 90% accuracy and an mAP of 0.91, outperforming standard CNN baselines and approaching YOLOv8 performance while requiring significantly fewer computational resources. YOLOv5 can also identify fractures in real time, and draw bounding boxes with confidence scores around the specific areas of the X-ray with fractures. OrthoTrace can also be deployed in local or cloud environments. The novelty of OrthoTrace lies in its incredibly rapid inference, while requiring very little computing power. Although promising, the system is limited by dataset size and lack of external validation. This system holds clinical significance as it can support rapid, reliable fracture detection in low-resource or emergency settings, making real-time deployment feasible on standard hospital hardware. Future work will focus on expanding datasets, and enhancing robustness across various medical scenarios. 2025 IEEE. -
UAV Security Analysis Framework
This study presents a framework that allows for various types of checks to detect weaknesses in UAV subsystems. The UAV testing process is automated and allows the operator only to select the types of checks or types of structural and functional characteristics that the operator wants to test. To ensure the possibility of automated verification, implemented databases are used, which include a catalog of structural characteristics, threats, vulnerabilities, and attacks. These catalogs are many-to-many related, and thanks to these links, it is possible to identify threats or vulnerabilities specific to a particular structural characteristic. In essence, such an architecture is a knowledge base based on an ontological model. Thanks to this architecture of the system, it is enough for the operator to determine what types of structural characteristics need to be checked and the system will give him information about the vulnerabilities of the UAV. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Analysis of the UAV Flight Logs in Order to Identify Information Security Incidents
The article discusses issues related to the analysis of the UAV flight logs to identify information security incidents that occurred during flights. Existing methods and tools for analyzing logs are described, and sources for obtaining logs are presented. In the main part of the article, first, the parameters important for the analysis are highlighted. The features of analyzing the values in the flight logs for the detection of two types of attacksGPS Spoofing and GPS Jamming are also given. For this purpose, the parameters that are most important for the detection of each of these attacks have been identified, systems of equations have been compiled to analyze these parameters, the calculations of which make it possible to detect the fact of attacks with high efficiency. The paper also presents the developed software that implements a number of functions that allow automating the analysis of flight logs, as well as determining the presence of information security incidents that occurred during the flight. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Impact of national income and public expenditure on employment and its public-private sector composition in indian economy
The paper focuses on the analysis of the impact of Gross National Income and Public Expenditure on total employment and its public and private sector components. It also examines the ratios of public and private sector employment to total employment and public sector to private sector employment. Results of summary statistics of employment, public and private sector employment, public expenditure, and gross national income are briefly discussed. Growth of total employment, public and private sector employment, gross national income and public expenditure is examined to determine the direction and magnitudes of inter-temporal changes. Growth trend of all three ratios is determined to detect and to anticipate the interrelations of change. Stationarity of time series of total employment, employment in public and private sectors, gross national income and public expenditure of Indian economy is evaluated by Random Walk Model and Dickey-Fuller test. Results show that the time series data of total, public and private sector employment approximate normal distribution with extremely low skewedness and concentration. The coefficients of variation of all 6 time series data are relatively very low. But the time series of GNI is non-stationary at 0.05 probability level, while time series of public expenditure displays negative trend. Negative change in private sector employment is determined by lagged private sector employment and private income/expenditure. Results of Engel-Granger test of co-integration show the variables to be well co-integrated in the chosen distributed lag models. 2021 DAV College. All rights reserved.