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Portrait segmentation using ensemble of heterogeneous deep-learning models
Image segmentation plays a central role in a broad range of applications, such as medical image analysis, autonomous vehicles, video surveillance and augmented reality. Portrait segmenta-tion, which is a subset of semantic image segmentation, is widely used as a preprocessing step in multiple applications such as security systems, entertainment applications, video conferences, etc. A substantial amount of deep learning-based portrait segmentation approaches have been developed, since the performance and accuracy of semantic image segmentation have improved significantly due to the recent introduction of deep learning technology. However, these approaches are limited to a single portrait segmentation model. In this paper, we propose a novel approach using an ensemble method by combining multiple heterogeneous deep-learning based portrait segmentation models to improve the segmentation performance. The Two-Models ensemble and Three-Models ensemble, using a simple soft voting method and weighted soft voting method, were experimented. Intersection over Union (IoU) metric, IoU standard deviation and false prediction rate were used to evaluate the performance. Cost efficiency was calculated to analyze the efficiency of segmentation. The experiment results show that the proposed ensemble approach can perform with higher accuracy and lower errors than single deep-learning-based portrait segmentation models. The results also show that the ensemble of deep-learning models typically increases the use of memory and computing power, although it also shows that the ensemble of deep-learning models can perform more efficiently than a single model with higher accuracy using less memory and less computing power. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
High-Movement Human Segmentation in Video Using Adaptive N-Frames Ensemble
A wide range of camera apps and online video conferencing services support the feature of changing the background in real-time for aesthetic, privacy, and security reasons. Numerous studies show that the Deep-Learning (DL) is a suitable option for human segmentation, and the ensemble of multiple DL-based segmentation models can improve the segmentation result. However, these approaches are not as effective when directly applied to the image segmentation in a video. This paper proposes an Adaptive N-Frames Ensemble (AFE) approach for high-movement human segmentation in a video using an ensemble of multiple DL models. In contrast to an ensemble, which executes multiple DL models simultaneously for every single video frame, the proposed AFE approach executes only a single DL model upon a current video frame. It combines the segmentation outputs of previous frames for the final segmentation output when the frame difference is less than a particular threshold. Our method employs the idea of the N-Frames Ensemble (NFE) method, which uses the ensemble of the image segmentation of a current video frame and previous video frames. However, NFE is not suitable for the segmentation of fast-moving objects in a video nor a video with low frame rates. The proposed AFE approach addresses the limitations of the NFE method. Our experiment uses three human segmentation models, namely Fully Convolutional Network (FCN), DeepLabv3, and Mediapipe. We evaluated our approach using 1711 videos of the TikTok50f dataset with a single-person view. The TikTok50f dataset is a reconstructed version of the publicly available TikTok dataset by cropping, resizing and dividing it into videos having 50 frames each. This paper compares the proposed AFE with single models and the Two-Models Ensemble, as well as the NFE models. The experiment results show that the proposed AFE is suitable for low-movement as well as high-movement human segmentation in a video. 2022 Tech Science Press. All rights reserved. -
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Powerlessness in the moral self: a social cognitive perspective on drug users
Powerlessness resides in devalued self-images of drug users. This study, drawing on social and moral psychology, examined the moral functioning of drug users compared to non-drug users. Self-reported data concerning moral identity and moral judgment on drug use were assessed and compared between groups. Drug users appeared to have significantly weaker moral identity centrality and pro-drug moral judgment than non-drug users. They also showed dissociation in the relationship between moral identity and moral judgment. As a result, the study proposed a moral identity model of drug use to better approach social cognitive powerlessness in drug users moral self. 2021 Taylor & Francis Group, LLC. -
Low-frequency pulse-jitter measurement with the uGMRT I: PSR J0437-4715
High-precision pulsar timing observations are limited in their accuracy by the jitter noise that appears in the arrival time of pulses. Therefore, it is important to systematically characterise the amplitude of the jitter noise and its variation with frequency. In this paper, we provide jitter measurements from low-frequency wideband observations of PSR J0437 4715 using data obtained as part of the Indian Pulsar Timing Array experiment. We were able to detect jitter in both the 300-500 MHz and 1 260-1 460 MHz observations of the upgraded Giant Metrewave Radio Telescope (uGMRT). The former is the first jitter measurement for this pulsar below 700 MHz, and the latter is in good agreement with results from previous studies. In addition, at 300-500 MHz, we investigated the frequency dependence of the jitter by calculating the jitter for each sub-banded arrival time of pulses. We found that the jitter amplitude increases with frequency. This trend is opposite as compared to previous studies, indicating that there is a turnover at intermediate frequencies. It will be possible to investigate this in more detail with uGMRT observations at 550-750 MHz and future high-sensitive wideband observations from next generation telescopes, such as the Square Kilometre Array. We also explored the effect of jitter on the high precision dispersion measure (DM) measurements derived from short duration observations. We find that even though the DM precision will be better at lower frequencies due to the smaller amplitude of jitter noise, it will limit the DM precision for high signal-to-noise observations, which are of short durations. This limitation can be overcome by integrating for a long enough duration optimised for a given pulsar. The Author(s), 2024. Published by Cambridge University Press on behalf of Astronomical Society of Australia. -
Synthesis of carbon nanomaterials from vegetables
This chapter looks into new horizons of sustainable nanotechnology developed through innovative carbocentrism that focuses on the development of carbon based nanomaterials from different categories of vegetables. This chapter is centered on the green synthesis of vegetable-derived sweet potato, garlic, lemon, and radish into carbon dots (CDs), graphene sheets, and carbon quantum dots through hydrothermal and aqueous extraction methods. To surpass traditional methods of nanomaterial synthesis, researchers are developing vegetable-derived nanomaterials that possess unique properties such as fluorescence and ranging surface functionalities. Such practices are recommended for reducing environmentally hazardous substances while upholding important eco-friendly principles and sustainable accountable nanotechnology. These methodologies address the misuse of dangerous substances and provides effective eco friendly approaches which emphasizenew direction towards sustainable nanotechnology. The versatility of these vegetable-derived carbon nanomaterials is evident in their applications, spanning from biomedical fields, such as drug delivery and bioimaging to environmental monitoring, particularly in the selective detection of metal ions. The advancements of medical technology are much needed in society today that is being more particular about green approaches and innovations. This willthe help low toxic and biocompatible nanomaterials live up to their full potential for eco-friendly biomedical technologies. This chapter serves as a comprehensive exploration of the synthesis, applications, and broader implications of carbon nanomaterials from vegetables, providing valuable insights into the evolving landscape of green nanotechnology. 2025 Elsevier Inc. All rights reserved. -
Design, Training, and Implementation of A New Individualized Education Plan (IEP) Format For Special Educators And Students With Intellectual Disabilities At Selected Special Schools
An individualized Education Plan (IEP) is a multidisciplinary, teamdeveloped plan required for every child receiving special education services. The researcher delved into concerns surrounding Individualized newlineEducation Programs (IEPs) for students with intellectual disabilities. Two significant hurdles were discovered: existing IEPs lacked essential intervention areas, and special education teachers felt inadequately newlineequipped to construct effective plans. newlineThe study tackled these concerns head-on through a multi-pronged approach. Firstly, a meticulous analysis of existing IEPs revealed crucial sections missing from intervention plans, hindering their effectiveness. newlineThis analysis served as the blueprint for crafting a more comprehensive IEP format that addressed the identified gaps and provided a robust framework for intervention. Next, the study focused on empowering special education teachers. Sixty special education teachers certified by the Rehabilitation Council of newlineIndia, participated in training sessions on the new format, undergoing a vital skills and knowledge upgrade in IEP development. This equipped them with the tools and understanding necessary to create more effective plans tailored to individual student needs. The theory then transitioned to practice. Students with intellectual newlinedisabilities were included in interventions based on the improved IEPs, with their progress closely tracked and evaluated. The results were highly promising. Teachers demonstrated a tangible improvement in knowledge, translating into their ability to create more effective IEPs. More importantly, students thrived with the enhanced format. Those involved in interventions using the improved IEPs exhibited significant progress in various domains, highlighting the positive impact of the new approach. The study culminated in key recommendations for further newlineimprovement. Ongoing teacher training sessions were suggested to ensure teachers remain updated on best practices and evolving methodologies. -
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Associations Between Early Life Adversity, Moral Development, and Psychopathology in Children and Adolescents: A Cross-Sectional Study
Introduction: Moral psychological development is shaped by socio-cultural and neurobiological factors, with the formation of conscience central to this process. Early Adverse Childhood Experiences (ACEs) have been linked to delays in moral development and increased risk of psychiatric disorders. This study examined how adversity affects conscience functioning, specifically the association between Psychopathological Interference (PI) and delays in Conscience Stages (CS) compared to youth raised in relative advantage. Methods: We analyzed 125 conscience-sensitive psychiatric interviews with youth admitted to a Psychiatric Residential Treatment Facility (PRTF). CS scores were compared with expected stages from community youth, using the Conscience Development Quotient (CDQ = CS attained CS expected 100). PI was rated on a Likert scale, incorporating full psychiatric evaluations, behavioral ratings, and DSM diagnoses. Multiple regression models examined the associations between CDQ, PI, and Clinical Global Assessment of Functioning (CGAF) scores, controlling for six covariates. Results: Participants (mean age, 14.2 years; 59% male, 41% female) exhibited significantly greater distress signals across conscience domains compared to community youth. No differences emerged by age at the onset of ACE. However, lower CDQ was associated with higher PI, earlier ACE onset, DSM Axis II disorders, and lower CGAF. Legal history and ACE count were not significant predictors. The model explained 22.7% of the variance in CDQ (p = 0.00018). Discussion: Findings highlight CDQ as a sensitive measure of developmental impact, beyond simply identifying red flags, consistent with prior ACE research. Retrospective design may limit sensitivity to ACE characteristics. Conclusion: Systematic conscience-sensitive interviewing, attuned to cultural and developmental contexts, may enhance clinical assessment of moral functioning. 2025, Bentham Science Publishers -
Self-Control and Decision-Making Skills as Predictors of College Enrollment: Role of Parental Influences
Self-control and planful decision-making can play a critical role in promoting academic outcomes. Nonetheless, little is known about how parental influences impact these noncognitive skills in promoting college enrollment. Using data from the National Longitudinal Study of Adolescent to Adult Health (Add Health), we examined adolescent self-control and decision-making skills (at wave 1) as predictors of college enrollment (at wave 3). Further, we assessed if the effect of parental influences (i.e., maternal academic involvement, maternal academic expectations, parental control/limit-setting, and parental education) on college enrollment was indirect and operated through the associations of parenting variables with adolescent self-control and planful decision making. Both self-control and decision-making skills significantly predicted college enrollment, controlling for age, gender, family income, and cognitive ability. Parental control/limit-setting and educational level had significant direct effects on college enrollment and were not significantly related to adolescent self-control or planful decision making skills. The effect of maternal academic involvement on college enrollment was indirect and operated through its associations with adolescent self-control and decision-making skills. The effect of maternal academic expectations on college enrollment was both direct and indirect, through its association with adolescent decision-making skills. Our findings suggest that individual and family-based interventions that target critical noncognitive skills, such as self-control and planful decision making, hold promise in promoting college enrollment. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Inhibiting extracellular cathepsin d reduces hepatic steatosis in spraguedawley rats y
Dietary and lifestyle changes are leading to an increased occurrence of non-alcoholic fatty liver disease (NAFLD). Using a hyperlipidemic murine model for non-alcoholic steatohepatitis (NASH), we have previously demonstrated that the lysosomal protease cathepsin D (CTSD) is involved with lipid dysregulation and inflammation. However, despite identifying CTSD as a major player in NAFLD pathogenesis, the specific role of extracellular CTSD in NAFLD has not yet been investigated. Given that inhibition of intracellular CTSD is highly unfavorable due to its fundamental physiological function, we here investigated the impact of a highly specific and potent small-molecule inhibitor of extracellular CTSD (CTD-002) in the context of NAFLD. Treatment of bone marrow-derived macrophages with CTD-002, and incubation of hepatic HepG2 cells with a conditioned medium derived from CTD-002-treated macrophages, resulted in reduced levels of inflammation and improved cholesterol metabolism. Treatment with CTD-002 improved hepatic steatosis in high fat diet-fed rats. Additionally, plasma levels of insulin and hepatic transaminases were significantly reduced upon CTD-002 administration. Collectively, our findings demonstrate for the first time that modulation of extracellular CTSD can serve as a novel therapeutic modality for NAFLD. 2019 by the authors. -
An empirical analysis of android permission system based on user activities
In today's world there has been an exponential growth among smart-phone users which has led to the unbridled growth of smart-phone apps available in Google play store, app store etc., In case of android application, there are many free applications for which the user need not shell out a penny to use the services. Here the magic word is "free" which entices millions of pliant people into installing those apps and giving unnecessary access to their data and device control. Current studies have shown that over 70% of the apps in market, request to gather data digressive to the most functions of apps that might cause seeping of personal data or inefficient use of mobile resources. Of late, couple of malignant applications gather unobtrusive information of the user through third-party applications by increasing their permissions to high-level on the Android Operating System. Android permission system provides, the user access to the third party apps and in return based on the permissions granted by the user, an app can access the related resource from the user's mobile. A user is bound to grant or deny permits during the installation of the application. For the most part, users don't focus on the asked permissions, or sometimes users do not understand the meaning of the permission and install the app on their device. They allow a way for attackers to perform the malicious task by demanding for more than expected set of permissions. These extra permissions permit the attacker to exploit the device and also retrieve sensitive information from it. In this research paper we describe how permission system security can create an awareness among the users that would assist them in deciding on permission grants. This improved and responsible user activities in Android OS can help the users in utilizing their device securely. 2018 Ankur Rameshbhai Khunt and P. Prabu. -
Big data analytics in tourism development and marketing: Theoretical perspectives on big data analytics in tourism marketing
The title of the suggested book chapter is " Theoretical Perspectives on Big Data Analytics in Tourism Marketing" and it is about the influence of big data analytics in the growth and promotion of tourism. It just shows how the AI and Metaverse can strategically use big data for better Market Segmentation and Customer behaviour analysis. This chapter looks at how metaverse technology allows tourists to participate in virtual experiences. Tourism companies can refine their marketing strategies, streamline operations, and provide value added experiences to their consumers by utilizing big data analytics. This Chapter underlines the power that big data has to change the tourism industry by enhancing decision making and spurring innovation in service provision. 2025 by IGI Global Scientific Publishing. All rights reserved. -
On estimation of extropy for non-negative data with application on uniformity testing
{Poisson weights-based density estimator is used to estimate the extropy function to the non-negative data}. The traditional class of nonparametric extropy estimators, typically constructed using kernel density estimators with symmetric kernels, is not well suited for non-negative data. To address this limitation, we propose two Poisson-weights-based density estimators that are naturally adapted to the non-negative domain. The asymptotic properties of the proposed estimators are rigorously established, providing theoretical support for their use. A comprehensive simulation study demonstrates that both estimators outperform their conventional kernel-based counterparts in terms of bias and mean squared error. Furthermore, we introduce uniformity tests based on extropy and obtain their critical values through simulation. The practical utility of the proposed methods is illustrated through analyses of real data sets. 2025 Informa UK Limited, trading as Taylor & Francis Group. -
Complicated Grief during COVID-19: An International Perspective
Cultures across the globe have evolved time-tested rituals to honor those who die and offer solace and support to survivors with the goal of helping them to accept the reality of the death, cope with the feelings of loss, adjust to life without the deceased, and find ways to maintain a connection to the memory of the deceased. The COVID-19 pandemic has disrupted these rituals and brought significant changes to the way we mourn. Specifically, public health responses to COVID-19 such as social distancing or isolation, delays or cancellations of traditional religious and cultural rituals, and shifts from in-person to online ceremonies have disrupted rituals and thus made it more difficult to access support and complete the psychological tasks typically associated with bereavement. This paper conceptualizes the common bereavement tasks including emotion-focused coping, maintaining a connection to the deceased, disengagement and reframing death and loss, and problemfocused coping. It provides examples of how the COVID-19 pandemic has altered mourning rituals across several cultures and religions and contributed to prolonged grief disorder as defined by the ICD-11 that includes depressive symptoms and post-traumatic stress. Early evidence suggested that the suddenness of loss, the social isolation, and the lack of social support often associated with COVID-19-related death are salient risk factors for complicated grief. As a consequence, psychological assessments, grief counseling, and mental health support are needed by families of patients who died from COVID-19. These services must be essential components of any comprehensive public health response to the pandemic. 2022 Hogrefe Publishing. -
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. -
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. -
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. -
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 -
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.