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Micromachining process-current situation and challenges
The rapid progress in the scientific innovations and the hunt for the renewable energy increases the urge for producing the bio electronic products, solar cells, bio batteries, nano robots, MEMS, blood less surgical tools which can be possible with the aid of the micromachining. This article helps us to understand the evolution and the challenges faced by the micromachining process. Micro machining is an enabling technology that facilitates component miniaturization and improved performance characteristics. Growing demand for less weight, high accuracy, high precision, meagre lead time, reduced batch size, less human interference are the key drivers for the micromachining than the conventional machining process. Owned by the authors, published by EDP Sciences, 2015. -
Being a therapeutic clown- an exploration of their lived experiences and well-being
Therapeutic clowning uses humor and play to minimize the stress for patients and their families during hospitalization. This study aims to explore the subjective meaning of therapeutic clowning through clowns perspective, understand why they continue clowning and interpret how it has impacted them. The research design takes a qualitative approach using phenomenological paradigm. Nine therapeutic clowns between 20 and 60years with clowning experience of 6months-4years from Compassionate Clowns, located at Bangalore were interviewed. The results reflected that the journey of being a therapeutic clown has been equally therapeutic for the clowns. Based on the thematic data analysis network, it was found that clowning has instilled many values in the way they think. It has given them a platform to learn new things from the children they clown. Therefore, looking at these results it could be said that therapeutic clowning serves as a medium for community service and in maintaining personal wellbeing. 2020, Springer Science+Business Media, LLC, part of Springer Nature. -
A Review of Deep Learning Methods in Cervical Cancer Detection
Cervical cancer is one of the most widespread and lethal malignancy that affects women aged 25 to 55 across the globe. Early detection of cervical cancer reduces burden of living and mortality drastically. Cervical cancer is caused through human papillomavirus transmitted sexually. Since the hereditary aspect is absent in cervical cancer, it can be cured completely if diagnosed early. Cervix cell image analysis is gold standard for classifying cervical cancer. Also known as pap smear, this histopathological test can provide dependable, and accurate diagnostic support. The current study examines the most recent research breakthroughs in deep learning models to classify cervical cancer. Three benchmark datasets are comprehensively described. Selective key classification models were implemented and comparative analysis was conducted on their performance. The findings of this study will allow researchers, publishers, and professionals to examine developing research patterns. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
A Novel Survey for Young Substellar Objects with the W-band Filter. V. IC 348 and Barnard 5 in the Perseus Cloud
We report the discovery of substellar objects in the young star cluster IC 348 and the neighboring Barnard 5 dark cloud, both at the eastern end of the Perseus star-forming complex. The substellar candidates are selected using narrowband imaging, i.e., on and off photometric technique with a filter centered around the water absorption feature at 1.45 ?m, a technique proven to be efficient in detecting water-bearing substellar objects. Our spectroscopic observations confirm three brown dwarfs in IC 348. In addition, the source WBIS 03492858+3258064, reported in this work, is the first confirmed brown dwarf discovered toward Barnard 5. Together with the young stellar population selected via near- and mid-infrared colors using the Two Micron All Sky Survey and the Wide-field Infrared Survey Explorer, we diagnose the relation between stellar versus substellar objects with the associated molecular clouds. Analyzed by Gaia EDR3 parallaxes and kinematics of the cloud members across the Perseus region, we propose the star formation scenario of the complex under influence of the nearby OB association. 2022. The Author(s). Published by the American Astronomical Society. -
Reducing the workload: AI as a support tool for human content moderators
Content moderation on digital platforms has evolved into a critical challenge due to the volume and complexity of user- generated content. This chapter explores the transformative role of Artificial Intelligence (AI) in addressing this issue, highlighting its capacity for scalability, efficiency, and consistent enforcement of policies. However, AI's limitations-such as interpreting context, navigating cultural nuances, and avoiding bias-underscore the necessity of human involvement. The hybrid AI- human approach emerges as the most effective model, leveraging AI's data- processing capabilities to alleviate workload while relying on moderators for nuanced judgments and ethical oversight. Through case studies of major platforms like Meta, Reddit, and YouTube, this chapter demonstrates the potential and challenges of integrating AI into moderation. It advocates for transparency, ethical AI practices, and research to refine hybrid models, aiming to balance efficiency, fairness, and trust. This chapter contributes to the discourse on sustainable, ethical frameworks in the digital age. 2025, IGI Global Scientific Publishing. All rights reserved. -
Multilevel CNN Based Hybrid Framework for Adaptive Credit Card Fraud Detection
Credit card fraud presents a substantial problem to financial organizations, as fast changing fraudulent activities necessitate advanced detection techniques. Conventional machine learning methods frequently encounter challenges with adaptability and precision in imbalanced datasets. This study presents a multilevel CNN-based hybrid architecture that combines deep convolutional networks with traditional ensemble classifiers for adaptive credit card fraud detection. The platform includes an adaptive learning module that facilitates ongoing model upgrades, guaranteeing responsiveness to emerging fraud trends. The system, evaluated using a benchmark Kaggle dataset, attained an accuracy of 99.48%, precision of 98.76%, recall of 99.05%, F1-score of 98.90%, and AUC-ROC of 99.91%, outperforming established baseline models such as Logistic Regression, Random Forest, and XGBoost. The suggested system's capacity to integrate deep feature extraction with hybrid classifiers yields enhanced detection efficiency, reduced false positives, and improved generalization. This research enhances fraud detection by overcoming the constraints of static models, rendering it applicable for real-time financial applications and adaptable to emerging threats. 2025 IEEE. -
Study on gravitational waves from binary mergers and constraints on the Hubble parameter
Einsteins general theory of relativity predicted the existence of gravitational waves (GWs), which offer a way to explore cosmic events like binary mergers and could help resolve the Hubble Tension. The Hubble Tension refers to the discrepancy in the measurements of the Hubble Constant, Ho, obtained through different methods and missions over various periods. By analyzing gravitational wave data, particularly from mergers that also emit light (electromagnetic radiation), such as Bright Sirens, we aim to reduce this tension. This paper will investigate the properties of GWs produced by these binary mergers and utilize a mathematical framework to tackle the Hubble Tension. Future advancements in gravitational wave astronomy, particularly with initiatives like LIGO-India and LISA, promise to enhance research outcomes. The ground-based LIGO-India will increase sensitivity and improve localization, while the space-based LISA will target lower frequency ranges of GWs, enabling the detection of signals from a wider array of sources. Indian Association for the Cultivation of Science 2025. -
Study on gravitational waves from binary mergers and constraints on the Hubble parameter
Einsteins general theory of relativity predicted the existence of gravitational waves (GWs), which offer a way to explore cosmic events like binary mergers and could help resolve the Hubble Tension. The Hubble Tension refers to the discrepancy in the measurements of the Hubble Constant, Ho, obtained through different methods and missions over various periods. By analyzing gravitational wave data, particularly from mergers that also emit light (electromagnetic radiation), such as Bright Sirens, we aim to reduce this tension. This paper will investigate the properties of GWs produced by these binary mergers and utilize a mathematical framework to tackle the Hubble Tension. Future advancements in gravitational wave astronomy, particularly with initiatives like LIGO-India and LISA, promise to enhance research outcomes. The ground-based LIGO-India will increase sensitivity and improve localization, while the space-based LISA will target lower frequency ranges of GWs, enabling the detection of signals from a wider array of sources. Indian Association for the Cultivation of Science 2025. -
Attributes of virtual influencers: Science mapping and future agenda
Virtual influencers, constructed using advanced computer-generated imagery and artificial intelligence, have emerged as disruptive forces in the realm of influencer marketing. In contrast to their human counterparts, virtual influencers provide brands with enhanced control, adaptability, and scalability, effectively engaging audiences on a global scale across various digital platforms. However, despite their increasing prevalence and commercial uptake, scholarly exploration into their attributes and impacts remains sporadic and underdeveloped. This study conducts a systematic literature review and bibliometric analysis of 172 peer-reviewed articles indexed in Scopus, employing the SPAR-4-SLR methodology to delineate the knowledge framework surrounding virtual influencers. By utilizing Biblioshiny from the bibliometrix package and VOSviewer for analytical and visualization purposes, this research reveals that attributes such as trustworthiness, expertise, attractiveness, similarity, and anthropomorphism dominate the current discourse. Conversely, attributes such as authenticity, source realness, emotional expression, novelty, autonomy, and interactivity have been identified as largely underexplored. The bibliometric analysis illustrates a marked increase in academic output since 2020, primarily concentrated in journals focused on marketing, social sciences, and human?computer interaction. Thematic mapping further highlights key areas of focus, with motor themes identified as anthropomorphism, virtual reality, and consumer engagement, indicating their foundational importance in the current landscape of virtual influencer research. Several research gaps persist, particularly in the areas of cross-cultural consumer responses to virtual influencers, ethical considerations, parasocial interactions, and comparative effectiveness analyses between virtual and human influencers. Overall, our study synthesizes the literature, systematically categorizes the key attributes of virtual influencers, and proposes a future research agenda aimed at advancing theoretical frameworks and managerial effectiveness. The insights gained here will guide researchers in identifying pertinent variables for empirical investigation and assisting practitioners in crafting strategies that enhance consumer trust, engagement, and purchase intention, ultimately navigating the evolving terrain of digital marketing more effectively. 2026, Malque Publishing. All rights reserved. -
Trends in virtual influencers (VIs): A bibliometric analysis and SPAR-4-SLR protocol
This study aims to comprehensively understand qualitative and quantitative information about the current trends in VIs. It examines 106 articles published in Scopus-indexed journals between 2020 and 2024. The analysis was done with the help of Biblioshiny, an R-developed online application from the Bibliometrix package, and VOSviewer software for analytical and visualization purposes. This study was conducted using the SPAR-4-SLR protocol. The findings showed that recent years have been more productive, and many authors have demonstrated their interest in studying the VIs. Recent trends are social media, virtual reality, marketing, social networking, etc. The study employs a systematic review and bibliometric analysis to extract valuable insights from the extensive body of literature. These insights suggested several areas for future research, providing a roadmap for future researchers to proceed with their research in this area. The comprehensive scientific cartography of the area has yet to be presented; therefore, this study aims to synthesize the current knowledge frameworks within the field and determine the dominant research patterns in the specific area of investigation. 2024, Malque Publishing. All rights reserved. -
Virtual Influencers (VIs): A Bibliometric Analysis and Future Agenda
This study aims to comprehensively understand qualitative and quantitative information about the emerging trends in VIs. It examines 106 articles published in Scopus-indexed journals between 2020 and 2024. The analysis was done with the help of Biblioshiny, an R-developed online application from the Bibliometrix package, and VOSviewer software for analytical and visualization purposes. This study was conducted using the SPAR-4-SLR protocol. The findings showed that recent years have been more productive, and many authors have demonstrated their interest in studying the VIs. Recent trends are social media, virtual reality, marketing, social networking, etc. The study employs a systematic review and bibliometric analysis to extract valuable insights from the extensive body of literature. These insights suggested several areas for future research, providing a roadmap for future researchers to proceed with their research in this area. The comprehensive scientific cartography of the area has yet to be presented; therefore, this study aims to synthesize the current knowledge frameworks within the field and determine the dominant research patterns in the specific area of investigation. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Study of vibrations in smart materials semiconductor under differential imperfect contact mechanism and nanoscale effect with electromechanical coupling effect
The work focuses on the transference of Love-type waves which are surface seismic waves that cause horizontal displacement perpendicular to the direction of propagation, in a multiferroic solid cylindrical structure, where the interface is assumed to be imperfect and made of a magneto-electro-elastic (MEE) structure. The analytical solution for the layer is obtained using the spatially variable quasi-classical technique which approximates complicated differential equations while maintaining their key physical properties. The coefficients of waves phase velocities and attenuation are greatly affected by different parameters as shown in the numerical example. In addition, a graphical comparison of electrical, magnetic, mechanical, magneto-mechanical, electromechanical, and magneto-electrical imperfections in electrically and magnetically open and short cases is presented. The phase velocity is higher in the electrically and magnetically open case as compared to the short case as shown in the results. Some major outcomes are summarized here: the bonding parameter is highly proportional to the phase velocity and inversely proportional to the attenuation coefficient, and imperfection parameters have a serious influence on the curve of phase velocity and attenuation coefficient. This theoretical study leads to the understanding of piezoelectric and piezomagnetic coupling and its potential application and design to sensors, actuators, energy harvesters, and nano-electronics. The novelty lies in the adoption of the quasi-classical method to approach solving differential equations using a polar coordinate system for the first time. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025. -
Detecting Abusive Comments in Mizo: A Machine Learning Approach for a Low-Resource Language
The detection of abusive language in online spaces is crucial for ensuring a safe digital environment, particularly for low-resource languages like Mizo. Mizo, a tonal Tibeto-Burman language spoken primarily in Mizoram, India, poses significant computational challenges due to its phonetic complexity and limited linguistic resources. This research presents a method based on machine learning for abusive comment detection in Mizo, addressing the lack of annotated datasets and specialized NLP tools. A structured pipeline involving data collection, preprocessing, feature engineering, and model evaluation was implemented. Our study compares the effectiveness of several conventional machine learning methods, such as Random Forest, Support Vector Machines (SVM), Logistic Regression, and XGBoost, against transformer-based models such as Multilingual BERT(mBERT) and MizBERT. According to experimental data, MizBERT achieves the highest accuracy and F1-score, outperforming all other models by a substantial margin. This work contributes to the development of computational tools for Mizo NLP, laying a foundation for automated moderation systems and fostering digital inclusivity for Mizo-Speaking communities. 2025 IEEE. -
Automatic Diagnosis of Autism Spectrum Disorder Detection Using a Hybrid Feature Selection Model with Graph Convolution Network
A neurodevelopmental disorder is called an autism spectrum disorder (ASD) that influences a persons assertion, interaction, and learning abilities. The consequences and severity of symptoms of ASD will vary from person to person; the disorder is mainly diagnosed in children aged 15years and older, and its symptoms may include unusual behaviors, interests, and social challenges. If it is not resolved at this stage, it will become severe in the coming days. So, in this manuscript, we propose a way to automatically tell if someone has ASD that works well by using a combination of feature selection and deep learning. Four phases comprise the proposed model: preprocessing, feature extraction, feature selection, and prediction. At first, the collected images are given to the preprocessing stage to remove the noise. Then, for each image, three types of features are extracted: the shape feature, texture feature, and histogram feature. Then, optimal features are selected to minimize computational complexity and time consumption using a new technique based on a combination of adaptive bacterial foraging optimization (ABFO), support vector machines-recursive feature elimination (SVM-RFE), minimum redundancy and maximum relevance (mRMR). Then, the graph convolutional network (GCN) classifier uses the selected features to identify an image as normal or autistic. According to the research observations, our models accuracy is enhanced to 97.512%. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Multi-atlas Graph Convolutional Networks and Convolutional Recurrent Neural Networks-Based Ensemble Learning for Classification of Autism Spectrum Disorders
Autism spectrum disorder (ASD) has an influence on social conversation and interaction, as well as encouraging people to engage in repetitive behaviors. The complication begins in childhood and persists through adolescence and maturity. Autism spectrum disorder has become the most common kind of childhood development worldwide. ASD hinders the capacity to interact, socialize, and build connections with individuals of all ages, and thus its early intervention is critical. This paper discusses some of the most recent approaches to diagnostics using convolutional networks and multi-atlas graphs for autism spectrum disorders. Also, several pre-processing approaches are elaborated. Graph convolutional neural networks (GCNs) to diagnose autism spectrum disorder (ASD) because of their remarkable effectiveness in illness prediction using multi-site data. Convolutional neural network (CNN) and recurrent neural networks (RNN) infrastructure studies functional connection patterns between various brain regions to find particular patterns to diagnose ASD. In our research, we implemented the GCN + CRNN ensemble method and achieved 89.01% accuracy based on resting-state data from the fMRI (ABIDE-II), a novel framework for detecting early signs of autism spectrum disorders is presented and discussed. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
An Efficient Security-Enabled Routing Protocol for Data Transmission in VANET Using Blockchain Ripple Protocol Consensus Algorithm
The security quality in Vehicular Ad-hoc NETworks (VANET) has improved as a result of recent developments in Intelligent Transportation Systems (ITS). However, within the current VANET system, providing a cheap computational cost with a high serving capability is a significant necessity. When a vehicle user goes between one Roadside Unit (RSU) to another RSU region in the current scenario, the current RSU periodically needs re-authentication of the vehicle user. This increases the computational complexity of the system. The gathering and broadcast of existing traffic event information by automobiles are critical in Vehicular Ad-hoc Networks (VANET). Traditional VANETs, on the other hand, have several security concerns. This work develops a blockchain-based authentication protocol to address the aforementioned difficulty. To address critical message propagation issues in the VANET, we invent a novel type of blockchain. We develop a local blockchain for exchanging real-world event messages among cars within a countrys borders, which is a novel sort of blockchain ideal for the VANET. We describe a public blockchain RPCA that records the trustworthiness of nodes and messages in such a distributed ledger suitable for secure message distribution. The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024. -
A Review on Deep Learning Algorithms in the Detection of Autism Spectrum Disorder
Autism spectrum disorder (ASD) is a neurodisorder that has an impact on how people interact and communicate with each other for the rest of their lives. Most autistic symptoms appear throughout the first two years of a child's life. This is why autism is called a behavioral disease. If you have a child with ASD, the problem starts in childhood and keeps going through adolescence and adulthood. Deep learning techniques are becoming more common in research on medical diagnosis. In this paper, there is an effort to see if convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory network (LSTM), and a fusion technique known as convolutional recurrent neural network (CRNN) can be used to detect ASD problems in a child, adolescents, and adults. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An Early Detection of Autism Spectrum Disorder Using PDNN and ABIDE I&II Dataset
The current study's objective was to use deep learning methods to separate valetudinarians amidst autism spectrum disorders (ASDs) from controls employing just the patients brain activation patterns from a dataset of large brain images. We examined brain imaging data from ASD patients from the global, multi-site ABIDE dataset (Autism Brain Imaging Data Exchange). Social impairments and repetitive behaviors are hallmarks of the brain condition known as autism spectrum disorder (ASD). ASD affects one in every 68 kids in the USA, as of the most recent data from the Disease Control Centers. To understand the neurological patterns that arose from the categorization, we looked into functional connectivity patterns that can be used to diagnose ASD participants precisely. The outcomes raised the state of the art by correctly identifying 72.10% of ASD patients in the sample vs. control patients. The classification patterns revealed an anti-correlation between the function of the brain's anterior and posterior regions; this anti-correlation supports the empirical data currently showing achingly ASD impedes communication between the livid brain's anterior and posterior areas. We found and pinpointed brain regions damn frolic, distinguishing ASD among typically developing reign according to our deep learning model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
AdvanDNN: Deep Neural Network Analysis of Neuroimaging for Identifying Vulnerable Brain Regions in Autism Spectrum Disorder
Exploring the neurological framework of autism spectrum disorder (ASD) presents a significant challenge due to its diverse manifestations and cognitive impacts. This study introduces an innovative deep learning approach, employing an advanced deep neural network (AdvanDNN) model to identify and analyze brain regions vulnerable to ASD. Utilizing the AAL116 brain atlas for anatomical standardization, our model processes a comprehensive set of neuroimaging data, including structural and functional MRI scans, to discern distinct neural patterns associated with ASD. The AdvanDNN model, with its robust deep learning architecture, was meticulously trained and validated, demonstrating a notable accuracy of 91.17% in distinguishing between ASD-affected individuals and controls. This marks an improvement over the state of the art, contributing a significant advance to the diagnostic processes. Notably, the model identified a pronounced anticorrelation in brain function between anterior and posterior regions, corroborating existing empirical evidence of disrupted connectivity within ASD neurology. The analysis further pinpointed critical regions, such as the prefrontal cortex, amygdala, and temporal lobes, that exhibit significant deviations from typical developmental patterns. These findings illustrate the potential of deep learning in enhancing early detection and providing pathways for intervention. The application of the AdvanDNN model offers a promising direction for personalized treatment strategies and underscores the value of precision medicine in addressing neurodevelopmental disorders. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Autism Spectrum Disorder: Automated Detection based on rs-fMRI images using CNN
Autism spectrum disorder (ASD) impacts approximately 1 in every 160 children globally and is classified as a neurodevelopmental condition. Image classification in neuroscience has advanced primarily due to convolutional neural networks (CNNs) and their capacity to provide better algorithms, more computing resources, and data. This study used a brain scan dataset to test the feasibility of utilizing CNN to detect ASD. Using functional connectivity patterns, the Autism Brain Imaging Exchange (ABIDE) data repository, which includes recordings of rest-state functional magnetic resonance imaging (rs-fMRI), the aim of using it was to distinguish between individuals who have Autism Spectrum Disorder (ASD) and those who are healthy controls. The proposed method effectively classified the two groups. According to the test findings, the suggested model has the ability to accurately detect ASD with a reliability rate of 92.22% when implemented on the ABIDE dataset using the CC200, CC400, and AAL116 brain atlases. The CNN model is computationally more efficient since it uses fewer parameters than other cutting-edge methods. 2023 IEEE.