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Segmentation technique for medical image processing: A survey
Segmentation is one of the popular and efficient technique in context to medical image analysis. The purpose of the segmentation is to clearly extract the Region of Interest from the medical images. The main focus of this paper is to review and summarize an efficient segmentation method. While doing the comparison study on segmentation methods using the Support Vector Machine, K-Nearest Neighbors, Random Forest and the Convolutional Neural Network for medical image analysis identifies that Convolutional Neural Network works efficiently for doing in-depth analysis. The Convolutional Neural Network can be used as segmentation technique for achieving the high accuracy on medical image analysis. 2017 IEEE. -
Segregating direct and indirect dimensions in ecosystem services valuation: The case of a coastal wetland ecosystem of south india
This paper provides insights into the multiple (direct and indirect) benefits of Kuttanad coastal wetland ecosystem in Kerala. Total annual direct ecosystem services generated from the wetlands are INR 8.45 billion or USD 0.11 billion per annum at 2020 prices. The estimates of the case study indicate that the annual value of indirect ecosystem services is thrice of direct provisioning services (Rs 22.52 billion or USD 0.31 billion per annum at 2020 prices). The valuation study would improve the knowledge and awareness of economic importance of wetland ecosystems among the various stakeholders including the policy makers of the society and their sustainable management to benefit the society. 2021 Ecological Society of India. All rights reserved. -
Segregation and researcher's positionality: Challenges of conducting policy ethnography in Southern polarized settings
Researchers conducting policy ethnography in conflict environments are faced with a valuable ethical dilemma is there an ethical standard to determine how a dataset should be pursued in the field? What if the method of pursuing data carries the potential of possibly disrupting one's rapport with the community and being perceived as a partisan ideologically driven researcher with ulterior motives? This question becomes more pronounced in socio-legal, conflict and public policy research in spatially polarized settings of the South. In these settings, knowledge is co-produced through one's own positionality and the nuances of grey areas that do not often feature in aggregated datasets. Scholarship on positionality has questioned whether scholars should explicate their position on the field by pointing towards the intentional or unintentional perpetuation of hierarchies. This paper situates itself in the positionality debate with reference to castelessness in socio-legal research through nine months of ethnographic fieldwork in a Southern spatially polarized setting. It grapples with an emerging contrasting view of whether researchers should at all engage in explicating their positionality. The paper argues that data is a socio-spatial product. It is to suggest that the production of data in conflict settings is informed by the spatial dynamics of social relations that emerge in the co-production of knowledge, and the researcher's reflexive positionality that itself impacts the outcome of data that emerges. 2025 The Author(s). Journal of Law and Society 2025 Cardiff University (CU). -
Seismic Activity-based Human Intrusion Detection using Deep Neural Networks
Human intrusion detection systems have found their applications in many sectors including the surveillance of critical infrastructures. Generally, these systems make use of cameras mounted on strategic locations for surveillance purposes. Cameras based detection systems are limited by line-of-sight, need regular maintenance and dependence of electricity for operations. These are all detrimental to the efficiency of these detection systems, especially in remote locations. To overcome these challenges, intrusion detection systems based on seismic activities have been in use. The seismic activities collected through geophones from the human footfalls can act as the input for these detection systems. This also poses a challenge as the data generated by the geophones for the seismic activities produced from footsteps are not always identical and hence not accurate. In this proposed work, a Deep Neural Network based approach has been used on the dataset collected from the geophones to effectively predict the presence of humans. The results gave a success rate with 94.86% accuracy with testing data and 92.00% accuracy with real-time data with the geophones deployed on an area covered with grass. 2022 IEEE. -
Seismic Performance Assessment of Reinforced Concrete Frames: Insights from Pushover Analysis
This paper offers a comprehensive exploration of the seismic response of Reinforced Concrete (RC) frames examined through pushover analysis. The frames analyzed are designed as per IS 13920 and IS 456 for different levels of earthquake intensities and different levels of axial loads. Nonlinear analysis techniques have gained prominence in assessing the response of RC frames, especially when subjected to extreme loading events or when accurate predictions of structural behavior are required beyond the linear elastic range. The study aims to delve into the structural behavior of RC frames under seismic influences, employing pushover analysis as the principal analytical tool. With a focus on assessing the effectiveness and reliability of pushover analysis, the research endeavors to elucidate the seismic performance of RC frames while considering their response to different seismic zones and axial loading scenarios. The methodology involves conducting a series of pushover analyses on RC frames using advanced structural analysis software. The results obtained are meticulously analyzed to discern the shear capacities and ultimate displacements of the frames, by investigating the displacement versus shear capacity relationship across varying seismic zones and axial loading scenarios. Through this comprehensive investigation, the paper aims to enhance our understanding of the seismic behavior of RC frames and will provide valuable insights for seismic design. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Selection of cobot for human-robot collaboration for robotic assembly task with Best Worst MCDM techniques
Since the first industrial robot was produced at the beginning of the 1960s, robotic technology has completely changed the sector. Industrial robots are made for various tasks, including welding, painting, assembling, disassembling, picking and placing printed circuit boards, palletizing, packing and labeling, and product testing. Finding flexible solutions that allow production lines to be swiftly re-planned, adjusted, and structured for new or significantly modified product development remains a significant unresolved problem. Today's Industrial robots are still mostly pre-programmed to do certain jobs; they cannot recognize mistakes in their work or communicate well with both a complicated environment and a human worker. Full robot autonomy, including organic interaction, learning from and with humans, and safe and adaptable performance for difficult tasks in unstructured contexts, will remain a pipe dream for the foreseeable future. Humans and robots will work together in collaborative settings such as homes, offices, and factory setups to execute various object manipulation activities. So, it is necessary to study the collaborative robots (cobots) that will play a key role in human-robot collaborations. Multiple competing variables must be considered in a thorough selection process to assess how well industrial cobots will work on an industrial working floor. To select a collaborative robot for the human-robot collaborative application, a straightforward multi-criteria decision-making (MCDM) methodology is based on the best-worst method (BWM). The ranking derived using the BWM method is displayed. The outcomes demonstrated the value of MCDM techniques for cobot selection. 2023 IEEE. -
Selection of specialization In management program a decision tree approach /
International Journal For Research In Engineering Application & Management, Vol.4, Issue 11, pp.96-103, ISSN No: 2454-9150. -
Selection of Tightened-Normal-Tightened sampling scheme under the implications of intervened Poisson distribution
Tightened-normal-tightened (TNT) sampling scheme is one of the most frequently used sampling schemes for making decisions about the finished product lots by examining certain samples from the lots. TNT sampling scheme includes two attribute sampling plans, one for tightened inspection and other for normal inspection along with switching rules. This paper introduces a procedure for TNT by incorporating two single sampling plans (SSP) under the conditions of intervened Poisson distribution (IPD) for the lots which may have a possibility of someintervention during the production process. The paper also assesses the performance of the proposed scheme procedure through its operating characteristic curves. Also, the unity value table is provided for certain parameters of specified producer's risk and consumer's risk for shop floor conditions. Further, the efficiency of proposed TNT scheme over the individual SSP under the conditions of IPD is demonstrated with illustrations. 2019 University of the Punjab. -
Selection of tightened-normal-tightened sampling scheme under the implications of intervened poisson distribution /
Pakistan Journal of Statistics and Operation Research, Vol.15, Issue 1, pp.129-140 -
Selective dual-mode detection of reactive oxygen species and metal ions by chemodosimetric vs. chelation pathways: fluorescence turn-on with OCl? and Zn2+/Mn2+, employing theoretical, practical, and bioimaging applications
An indole-coupled diaminomaleonitrile-based fluorescent chemosensor IMA has been designed and developed for the selective detection of ROS (OCl?) and metal ions Zn2+ and Mn2+via chemodosimetric and chelation pathways respectively. The selective sensing of OCl? is induced by a method of oxidatively cleaving of the imine bond of IMA, forming free indole aldehyde, which results in a 21-fold enhancement of fluorescence at 521 nm, with a detection limit of 2.8 M. On the other hand, the selective binding of IMA with Zn2+ and Mn2+ results in chelation-induced enhanced fluorescence (CHEF) and increased intermolecular charge transfer (ICT), leading to a 4-fold and 3-fold fluorescence enhancement at 432 nm and 435 nm, with the detection limits of 12.71 M and 17.34 M, respectively. UV-vis spectroscopy, fluorescence, DFT study, mass spectra, 1H-NMR analysis, and Job's plot analysis have been used to validate the sensing mechanism of IMA with OCl?, Zn2+, and Mn2+. For practical applications, the binding of IMA with OCl? has been utilized in the detection of commercial samples like bleaching powder and water analysis. Bio-imaging studies were conducted with IMA in the presence of OCl? and Zn2+ using green gram seeds in a physiological medium. 2025 The Royal Society of Chemistry. -
Selective subset of relative density feature extraction algorithm for unconstrained single connected handwritten numeral recognition /
Australian Journal of Basic and Applied Sciences, Vol.8, Issue 6, pp.315-321, ISSN No: 1991-8178. -
Self compacting concrete for slip form paving
International Journal of Research in Engineering and Technology, Vol-4(7), ISSN-2319-1163 -
Self Compacting Concrete for Slip from Paving
Volume:04, Issue:07, July -
Self in schizophrenia: Current issues and future directions
Background: The objective of this review is to discuss the current advancements, and critical issues, in the area of studying disturbances of self in schizophrenia. The critical and systematic review of the self in schizophrenia is significant because it has been regarded as a prodrome and a predictor of the development of future psychosis. In addition, it has been found to be over and above clinical symptoms and is common in people with schizophrenia. Methodology: A systematic electronic literature search was done using PubMed, MEDLINE, and PMC (PubMed Central) databases were searched systematically, and relevant articles published in English peer-reviewed journals were selected. Results: The findings were discussed, and critical analysis of the studies revealed methodological and conceptual issues in the literature studying self in schizophrenia. Conclusion: The review has concluded with the discussion on future directions in terms of research and clinical applications. 2018 Archives of Mental Health | Published by Wolters Kluwer - Medknow. -
Self lubricating property of MWCNT in AA2219 composites during high energy ball milling
Revolutions in nanotechnology enabled the development of advanced nanocomposites with superior properties for engineering applications especially in automotive and aerospace industries. Among this carbonaceous nano materials like MWCNT have got more attention. Addition of MWCNT in metal matrix results in retardation of friction coefficient and improvement on other mechanical properties based on its dispersion. MWCNT won't have sufficient space to occupy over the powder surface, when the addition is beyond a limit and acts as a solid lubricant during milling. Investigations on self lubricating property during milling were done by using scanning electron microscope, X-ray diffraction and powder density. Uniform dispersion was the bottleneck to utilize their attractive properties of the reinforcement. An attempt had been done for a uniform dispersion during premixing process using a combination of ultra-sonication, magnetic and mechanical stirring followed by high energy ball milling. 2019 Elsevier Ltd. -
Self Risk Assessment Model Embedded with Conversational User interface for Selection of Health Insurance Product
In this research, we propose a dynamic model that works through Human-Computer Interaction to facilitate a smooth customer experience for health insurance prospects. The model facilitates the prospects to self assess their health risks. The integration of Conversational User interface, such as Mobile User Interface, Graphic User Interface and Bots with transcoder permits seamless use of the model by any category of prospects, irrespective of their language. Moreover, the model also helps the visually impaired person to interact without any hassle with the presence of a transcoder that permits conversion of text into speech and vice versa. The learner model comprises of the Prospects' detail module and Risk Assessment modules. The Prospects' detail module collects data from the predefined list. The risk assessment module profiles and assesses the risk based on the data inputted in the Prospects' detail module. The risk assessment level module categorizes the level of risk as low, moderate or high for each prospect depending on the total risk exposure level. The total risk exposure level is computed based on the pre-defined threshold. This model aids the prospect in determining the risk level and thereby facilitates self-selection of health insurance policy, thus reducing over reliance on the insurer. This model helps the prospect to take an independent purchase decision. 2022 IEEE. -
Self-adaptive Butterfly Optimization for Simultaneous Optimal Integration of Electric Vehicle Fleets and Renewable Distribution Generation
Fuel prices and environmental concerns have prompted an increase in the use of electric vehicle (EV) technology in recent years. Charging stations (CSs) are a great way to support this shift to sustainability. This has increased the demand for EV charging on electrical distribution networks (EDNs). However, optimal EV charging stations along with renewable energy sources (RES) integration can maintain EDN performance. This paper proposes a novel hybrid approach based on self-adaptive butterfly optimization algorithm (SABOA) for optimal integration of EV CSs and RES problems under various EV load growth scenarios. A multi-objective function is created from distribution losses, GHG emissions, and VSI. The ideal locations for CSs and RES are found using SABOA while minimizing the proposed multi-objective function. The simulation results on IEEE 33-bus EDN validate the suggested technique's superiority in terms of global optima. This type of hybrid strategy is required for optimal real-time integration of EV CSs and RES, taking into account emerging high EV load penetrations. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Self-assembled free nanocarrier prodrugs based on camptothecin and dihydroartemisinin exhibit accumulation and improved anticancer efficacy
Small molecule targeted inhibitor therapies often have several drawbacks, including limited oral bioavailability, quick metabolism, toxic effects that limit dosage, and poor water solubility. This study aims to develop a nanodrug self-delivery system that does not require a carrier by utilizing the self-assembly of camptothecin (CPT) and dihydroartemisinin (DHA). CPT/DHA nanoparticles (NPs) with varying diameters can be synthesized without requiring further carrier materials or chemical modifications by changing the CPT-to-DHA ratio (10:1, 5:1, 2:1, 1:1). Even more crucially, CPT/DHA NPs generate an AIE impact when they self-assemble. CPT/DHA NPs are used for cell tracking and bioimaging fluorescent probes. We chose CPT/DHA NPs (2:1) with a size of approximately 140nm for the anticancer examinations. The A549 cells were used to assess the cytotoxicity, morphological changes by biochemical staining methods and apoptosis by flow cytometric techniques of CPT/DHA NPs. Finally, in vitro anticancer research proved that CPT/DHA NPs are biocompatible and have strong synergistic anticancer properties. 2024 International Union of Biochemistry and Molecular Biology, Inc. -
Self-assembled free nanocarrier prodrugs based on camptothecin and dihydroartemisinin exhibit accumulation and improved anticancer efficacy
Small molecule targeted inhibitor therapies often have several drawbacks, including limited oral bioavailability, quick metabolism, toxic effects that limit dosage, and poor water solubility. This study aims to develop a nanodrug self-delivery system that does not require a carrier by utilizing the self-assembly of camptothecin (CPT) and dihydroartemisinin (DHA). CPT/DHA nanoparticles (NPs) with varying diameters can be synthesized without requiring further carrier materials or chemical modifications by changing the CPT-to-DHA ratio (10:1, 5:1, 2:1, 1:1). Even more crucially, CPT/DHA NPs generate an AIE impact when they self-assemble. CPT/DHA NPs are used for cell tracking and bioimaging fluorescent probes. We chose CPT/DHA NPs (2:1) with a size of approximately 140nm for the anticancer examinations. The A549 cells were used to assess the cytotoxicity, morphological changes by biochemical staining methods and apoptosis by flow cytometric techniques of CPT/DHA NPs. Finally, in vitro anticancer research proved that CPT/DHA NPs are biocompatible and have strong synergistic anticancer properties. 2024 International Union of Biochemistry and Molecular Biology, Inc. -
Self-Care, burnout, and compassion fatigue in oncology professionals
Context: With the rising number of cancer cases in India, the stress levels of the treating team have increased. It has affected their self-care and made them susceptible to problems like burnout and compassion fatigue that adversely affect the quality of patient care. Aims: The aim of the study was to assess and compare the levels of burnout, compassion fatigue, and self-care in three groups of oncology professionals (clinical oncologists, nurses, and psychologists). Settings and Design: The study included 134 oncology professionals working in New Delhi, Bengaluru, and Mumbai. Methods and Material: Sociodemographic data sheet, Professional Quality of Life Scale V and Self-Care Assessment Worksheet were used. Statistical Analysis Used: Kruskal-Wallis, Mann-Whitney U test, and Correlation Analysis. Results: The majority of the professionals reported moderate levels of burnout (60.4%) and compassion fatigue (56%). Oncology nurses reported an elevated risk as they scored significantly higher on these domains and had a lower degree of self-care. Interestingly, psychologists reported comparatively lower levels of burnout and compassion fatigue, despite the fact that they interact with the patients at a deeper level, looking after their psychological and emotional needs. Young age and a poor degree of self-care were identified as major risk factors. Conclusions: The moderate levels of burnout and compassion fatigue, though not severe, are a cause of concern and cannot be overlooked. The study highlights the need for self-care in this regard and suggests that individual and institutional level interventions, particularly for nurses and young professionals, would prove useful. 2020 Wolters Kluwer Medknow Publications. All rights reserved.