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A pharmacognostic approach, including phytochemical and GC-MS analysis, targeted towards the authentication of Strobilanthes jomyi P. Biju, Josekutty, Rekha & J.R.I.Wood
The genera Strobilanthes Blume have a rich history in therapeutic culture all over the world. Asian countries like India, China, Myanmar and Thailand still use Strobilanthes genus-based medicinal preparations for various diseases. Strobilanthes jomyi is a newly discovered species from Kerala, India. Some tribal communities of Kasaragod district still use S. jomyi leaf extract as a wound healing medication. The current study aims to investigate the pharmacognostic, phytochemical and GC-MS analysis of the leaves, stems and roots of S. jomyi. The microscopic, macroscopic, organoleptic, fluorescent, phytochemicals and GC-MS analysis of the leaves, stem, and root of S. jomyi were estimated using various standard protocols. The macroscopic and microscopic characters of leaves revealed the presence of non-glandular trichomes with paracytic stomata in the leaves. The transverse section of the stem and petiole showed the presence of raphides and the root showed the presence of tannin cells. Cystoliths were observed only in the petiole. Powder morphology of leaves, stems and roots revealed the presence of fibers, trichomes, palisade cells, spiral xylem vessels, bordered pit vessels and raphides. The vegetative part of S. jomyi powder exhibited various fluorescent coloration based on numerous chemical treatments along with different tastes, smells, colors and textures by organoleptic assays. Qualitative phytochemical analysis of different vegetative parts revealed the presence of flavonoids and other phytochemicals. GC-MS study revealed that lupeol a significant bioactive compound was present in all the vegetative parts of S. jomyi. The results acquired from this study can be used for the standardization, identification, quality and purity check of plant samples. The Author(s). -
A Phased approach to solve the University Course Scheduling System
International Journal of Computational Engineering Research Vol.3, Issue 4, pp. 258-261, ISSN No. 2250-3005 -
A phenomenological exploration of Indian women's body image within intersecting identities in a globalizing nation
The goal of the study was to examine Indian women's body image experiences utilizing an intersectional framework. Using phenomenological method, the study attempted to explore how experiences of gender oppression intersect with salient social identities to produce experiences of body dissatisfaction in Indian women. Thirty-Five Indian women in the age group 1840 years participated in semi-structured interviews. Overall, women experienced and discussed their bodies in terms of physical features they liked and disliked. Three themes emerged that comprised body image experiences of Indian women- (a) Beautiful, thin and fair- three social imperatives for women, (b) Internalization and (c) Body image management. Each of these impacted women negatively and contributed to greater body monitoring, increased indulgence in unhealthy behaviours and heightened body dissatisfaction. Women also discussed coping techniques for managing such experiences. Researchers and practitioners are encouraged to take into account culturally constructed beauty norms and unique socio-cultural factors for Indian women that determine body image. Findings are interpreted in the context of evolving socio-cultural norms that have recolonised Indian women's embodiment in a globalizing nation. 2023 Elsevier Ltd -
A Pilot Feasibility Study of Reconnecting to Internal Sensations and Experiences (RISE), a Mindfulness-Informed Intervention to Reduce Interoceptive Dysfunction and Suicidal Ideation, among University Students in India
Although 20% of the worlds suicides occur in India, suicide prevention efforts in India are lagging (Vijayakumar et al., 2021). Identification of risk factors for suicide in India, as well as the development of accessible interventions to treat these risk factors, could help reduce suicide in India. Interoceptive dysfunctionor an inability to recognize internal sensations in the body has emerged as a robust correlate of suicidality among studies conducted in the United States. Additionally, a mindfulness-informed intervention designed to reduce interoceptive dysfunction, and thereby suicidality, has yielded promising initial effects in pilot testing (Smith et al., 2021). The current studies sought to replicate these findings in an Indian context. Study 1 (n = 276) found that specific aspects of interoceptive dysfunction were related to current, past, and future likelihood of suicidal ideation. Study 2 (n = 40) was a small, uncontrolled pre-post online pilot of the intervention, Reconnecting to Internal Sensations and Experiences (RISE). The intervention was rated as highly acceptable and demonstrated good retention. Additionally, the intervention was associated with improvements in certain aspects of interoceptive dysfunction and reductions in suicidal ideation and eating pathology. These preliminary results suggest further testing of the intervention among Indian samples is warranted. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
A Pilot Study of the DREAMS Program: A Community Collaborative Intervention for the Psychosocial Development of Middle School Students
The purpose of this study was to pilot the DREAMS (Desire, Readiness, Empowerment, Action, and Mastery for Success) program, a community-collaborative, after-school intervention program designed specifically to address the holistic developmental needs of students at school. The author originally developed and implemented the program in Kerala, India, and later redesigned it for American school students. Combining the theories of Vygotsky and Erikson, the DREAMS model emphasizes the impact of the community on the development of children. This study evaluates the effects of a summer camp, the primary intervention of a three-year program, on the self-worth, self-esteem, and self-concept of 20 middle school students in Northeast Louisiana. After students attended the week-long program, the most significant improvements were observed in self-esteem and self-worth. Further longitudinal or comparative experimental research on the complete design would provide stronger evidence to draw more substantive conclusions. (2024), (California State University). All rights reserved. -
A Pilot Study on Detection of Microplastics for Environmental Monitoring Using Inland Lakes as Ecological Indicators
The waterbodies of a city play a major role in its biodiversity and ecological well-being. The main aspect of this study was to select lakes close to urban areas that are affected due to garbage dumping or have wastewater treatment plants inlets in them and check for microplastics (MPs) presence in them. Seetharampalya and Puttenahalli lakes in Bangalore both showed the presence of microplastics in their water and bank sediment soil samples, which were segregated by the wet peroxide oxidation process. In scanning electron microscopy (SEM) analysis, the microplastics segregated from the water of Seetharampalya lake were found to be clumped and in clusters of uneven form and shape. Microplastics extracted from the soil of Seetharampalya lake were found to have sheet, like structures with occasional dumps or clusters. The microplastics sorted out from Puttenahalli lake water were uneven and had roughly rectangular structures. The soil microplastics recovered from Puttenahalli lake were found to be sheaths of globular masses. The energy dispersive spectroscopy (EDS) analysis majorly showed presence of carbon and oxygen. In Fourier transform infrared spectroscopy (FTIR) analysis, characteristic peaks at 719/cm and 1469/cm were observed. Similarly, in x-ray diffraction (XRD), the 26 values around 20 could be seen in all four samples. This is the first reported study of microplastics in these lakes of Bangalore. 2024 - Kalpana Corporation. -
A post covid machine learning approach in teaching and learning methodology to alleviate drawbacks of the e-whiteboards
Deep learning has paved the way for critical and revolutionary applications in almost every field of life in general. Ranging from engineering to healthcare, machine learning and deep learning has left its mark as the state-of-the-art technology application which holds the epitome of a reasonable high benchmarked solution. Incorporating neural network architectures into applications has become a common part of any software development process. In this paper, we perform a comparative analysis on the different transfer learning approaches in the domain of hand-written digit recognition. We use two performance measures, loss and accuracy. We later visualize the different results for the training and validation datasets and reach to a unison conclusion. This paper aims to target the drawbacks of the electronic whiteboard with simultaneous focus on the suitable model selection procedure for the digit recognition problem. 2021 Tamkang University. All Rights Reserved. -
A POWERFUL ITERATIVE APPROACH for QUINTIC COMPLEX GINZBURG-LANDAU EQUATION within the FRAME of FRACTIONAL OPERATOR
The study of nonlinear phenomena associated with physical phenomena is a hot topic in the present era. The fundamental aim of this paper is to find the iterative solution for generalized quintic complex Ginzburg-Landau (GCGL) equation using fractional natural decomposition method (FNDM) within the frame of fractional calculus. We consider the projected equations by incorporating the Caputo fractional operator and investigate two examples for different initial values to present the efficiency and applicability of the FNDM. We presented the nature of the obtained results defined in three distinct cases and illustrated with the help of surfaces and contour plots for the particular value with respect to fractional order. Moreover, to present the accuracy and capture the nature of the obtained results, we present plots with different fractional order, and these plots show the essence of incorporating the fractional concept into the system exemplifying nonlinear complex phenomena. The present investigation confirms the efficiency and applicability of the considered method and fractional operators while analyzing phenomena in science and technology. 2021 The Author(s). -
A pragmatic study on heuristic algorithms for prediction and analysis of crime using social media data
Advancement in technology and Social media has grown to become one amongst the foremost powerful communication channels in human history and this is where individuals are sharing their perspectives, thoughts, suppositions, and feelings. Law enforcement units are having hard time fighting crime with evergrowing population, regional issues and political con-sequences. The adoption of social media data for crime analysis is increasing day by day. Crime analysis can help use the resources wisely. A crime prediction alerts the department at the right time to focus their staff with better equipment in suspected areas. Crime analysis prevents threats to life and money loss in terms of damage. In recent days, the collection of crime data from different heterogeneous sources becomes a primary step for the crime analysis and prediction. In this paper Overview of Heuristic Based Crime Prediction and Analysis algorithms identified by different authors. Also, various sources of social media used for analysis and prediction are also reviewed in detail. This information can be considered for one of the prominent asset for crime investigation through social media data procedure and also, we had identified the different algorithms and research gaps of that algorithms with related to crime analysis and prediction. 2019, Institute of Advanced Scientific Research, Inc. All rights reserved. -
A Precise Computational Method for Hippocampus Segmentation from MRI of Brain to Assist Physicians in the Diagnosis of Alzheimer's Disease
Hippocampus segmentation on magnetic resonance imaging is more significant for diagnosis, treatment and analyzing of neuropsychiatric disorders. Automatic segmentation is an active research field. Previous state-of-the-art hippocampus segmentation methods train their methods on healthy or Alzheimer's disease patients from public datasets. It arises the question whether these methods are capable for recognizing the hippocampus in a different domain. Therefore, this study proposes a precise computational method for hippocampus segmentation from MRI of brain to assist physicians in the diagnosis of Alzheimer's disease (HCS-MRI-DAD-LBP). Initially, the input images are pre-processed by Trimmed mean filter for image quality enhancement. Then the pre-processed images are given to ROI detection, ROI detection utilizes Weber's law which determines the luminance factor of the image. In the region extraction process, Chan-Vese active contour model (ACM) and level sets are used (UACM). Finally, local binary pattern (LBP) is utilized to remove the erroneous pixel that maximizes the segmentation accuracy. The proposed model is implemented in MATLAB, and its performance is analyzed with performance metrics, like precision, recall, mean, variance, standard deviation and disc similarity coefficient. The proposed HCS-MRI-DAD-LBP method attains in OASIS dataset provides high disc similarity coefficient of 12.64%, 10.11% and 1.03% compared with the existing methods, like HCS-DAS-MLT, HCS-DAS-RNN and HCS-DAS-GMM and in ADNI dataset provides high precision of 20%, 9.09% and 1.05% compared with existing methods like HCS-MRI-DAD-CNN-ADNI, HCS-MRI-DAD-MCNN-ADNI and HCS-MRI-DAD-CNN-RNN-ADNI, respectively. 2022 World Scientific Publishing Europe Ltd. -
A precise method for gender cataloguing using a minimum distance classifier /
The International Journal of Engineering and Science, Vol-3 (2), pp. 1-4. ISSN (p)-2319-1805 ISSN (e)-2319-1813 -
A prediction technique for heart disease based on long short term memory recurrent neural network
In recent years, heart disease is one of the leading cause of death for both women and men. So, heart disease prediction is considered as a significant part in the clinical data analysis. Standard data mining techniques like Support Vector Machine (SVM), Naive Bayes and other machine learning techniques used in the earlier research for heart disease prediction. These methods are not sufficient for effective heart disease prediction due to insufficient test data. In this research, Bi-directional Long Short Term Memory with Conditional Random Field (BiLSTM-CRF) has been proposed to increase the efficiency of heart disease prediction. The input medical data were analyzed in a bidirectional manner for effective analysis, and CRF provided the linear relationship between the features. The BiLSTMCRF method has been tested on the Cleveland dataset to analyze the performance and compared with existing methods. The results showed that the proposed BiLSTM-CRF outperformed the existing methods in heart disease prediction. The average accuracy of the proposed BiLSTM-CRF is 90.04%, which is higher than the existing methods. 2020 by the authors. -
A primary study on the degradation of low-density polyethylene treated with select oxidizing agents and starch
Polyethylene has become an integral part of our contemporary lives. The neoteric versatile nature of polyethylene is used in constructing various applications. Out of the plastic waste discarded, 60% of the plastic waste enters landfills. The polyethylene discarded in the soil and water on exposure to the environment forms macroplastics (>2.5 cm), mesoplastics (5 mm-2.5 cm) and microplastics (<5 mm). Microplastics in the water and soil are observed to have lethal and ecotoxicological effects on aquatic and terrestrial organisms. They enter the food chain and permeate into the food that one eats. In order to address this impending concern, the present study aimed to treat plastics to form a degradable, safe and earthy material. The dissolved polyethylene was treated with starch and was made to react with oxidizing agents such as hydrogen peroxide, nitric acid and acetic acid to lower its inert ability to withstand its degradation. The effect of starch and oxidizing agents on dissolved low density polyethylene was subsequently analysed. The analysis of treated polyethylene showed a decrease in its crystallinity percentage by 6.19 and an increase in its functional groups on reaction with solvent trichloroethylene made to react with starch and oxidizing agents. In the present research, tests were conducted to obtain the various methods that can be utilized to reverse the inert ability of polyethylene. The prevailing recycling model that uses antioxidation techniques is counterproductive since it was found that such techniques appeared to make the polyethylene more resistant to further degradation. In this study, the polyethylene was dissolved in the solvents, such as xylene and trichloroethylene, to make the polyethylene more susceptible to reactants and hence a viable model for treating polyethylene. : Author (s). Publishing rights @ ANSF. -
A privatised approach in enhanced spam filtering techniques using TSAS over cloud networks
Major problem over cloud networks is the effect of malicious code that protrudes its own activity without intend of network user in resource sharing. One such activity is the spam-filtering techniques which assumes the data with training and testing sets and also rely on fundamental classification through distribution. A privatised spam filtering approach is a classic problem which automatically recognises user context and incoming mail information relevance. To filter mail contents learning based methods, probabilistic based method trying to improve their accuracy but they cannot attain an improvement in identifying suspicious contents and also in segregating legitimate mail entries. Here a novel representation of structured abstraction scheme (SAS) used to generate abstraction in e-mail process using HTML tag content in e-mail and its algorithm for filtering such process of spam filtering is depicted. In this SAS methodology near duplicate matching process with HTML tag ordering will be processed and newly assigned position ordering were deliberated. The experimental setup shows that there will be a great improvement while filtering spam in accuracy of e-mail content while sharing in cloud networks. Copyright 2022 Inderscience Enterprises Ltd. -
A probabilistic inference algorithm for early detection of age related macular degeneration
Age Related Macular Degeneration or ARMD is a retinal disorder that causes blindness over people of older age group. ARMD is associated with age and is a leading cause of blindness around the world. There is no specific medicine to fully cure ARMD but its development can be controlled by regular exercises and a healthy lifestyle if it is detected early. With a rising population of old age group of people, it becomes important to detect ARMD as early as possible in order to contain its development further. This research attempts to develop an algorithm based on probabilistic inference through Bayesian Network by analyzing large datasets collected from previous cases where datasets include elements of risk factors that could cause ARMD along with eye images. Unlike most of the approaches in detecting ARMD this work not only analyses eye images but also includes analysis of various factors causing the disorder. To include the study and analysis of the presence of factors causing ARMD is sensible because those factors are good indicators when the need is an early detection. 2020, Engg Journals Publications. All rights reserved. -
A Progressive UNDML Framework Model for Breast Cancer Diagnosis and Classification; [Un modelo marco progresivo UNDML para el diagntico y clasificaci del ccer de mama]
According to recent research, it is studied that the second most common cause of death for women worldwide is breast cancer. Since it can be incredibly difficult to determine the true cause of breast cancer, early diagnosis is crucial to lowering the diseases fatality rate. Early cancer detection raises the chance of survival by up to 8 %. Radiologists look for irregularities in breast images collected from mammograms, X-rays, or MRI scans. Radiologists of all levels struggle to identify features like lumps, masses, and micro-calcifications, which leads to high false-positive and false-negative rates. Recent developments in deep learning and image processing give rise to some optimism for the creation of improved applications for the early diagnosis of breast cancer. A methodological study was carried out in which a new Deep U-Net Segmentation based Convolutional Neural Network, named UNDML framework is developed for identifying and categorizing breast anomalies. This framework involves the operations of preprocessing, quality enhancement, feature extraction, segmentation, and classification. Preprocessing is carried out in this case to enhance the quality of the breast picture input. Consequently, the Deep U-net segmentation methodology is applied to accurately segment the breast image for improving the cancer detection rate. Finally, the CNN mechanism is utilized to categorize the class of breast cancer. To validate the performance of this method, an extensive simulation and comparative analysis have been performed in this work. The obtained results demonstrate that the UNDML mechanism outperforms the other models with increased tumor detection rate and accuracy. 2024; Los autores. -
A proposed framework for an appropriate governance system to develop smart cities in India
The Government of India has undertaken a novel step towards building new smart cities as well as transforming some of its existing cities into smart cities. However, tension relating to the governance of smart cities has emerged. Therefore, a mixed-methods approach was used based on a perception survey, case studies, and discussions with stakeholders and experts, to examine the current governance challenges in transforming existing cities into smart cities, and to explore various perspectives to propose a framework for an appropriate governance system for developing smart cities in India. The findings suggested that the current executive-led governance system, with special-purpose vehicles (SPVs) under the control of the state governments as the promoters of smart city development, might not lead to the smart governance system envisaged but, rather, add confusion and conflict, and undermine the constitutionally mandated, legislative-led urban local bodies. The argument in this article is for a people-centric, balanced governance approach with strengthened urban local bodies, enabled by advanced digital technology and the constructive participation of different social solidarities, in which the SPVs would act as the intellectual and executive wing of the urban local bodies. 2023 Regional Studies Association. -
A proposed framework for crop yield prediction using hybrid feature selection approach and optimized machine learning
Accurately predicting crop yield is essential for optimizing agricultural practices and ensuring food security. However, existing approaches often struggle to capture the complex interactions between various environmental factors and crop growth, leading to suboptimal predictions. Consequently, identifying the most important feature is vital when leveraging Support Vector Regressor (SVR) for crop yield prediction. In addition, the manual tuning of SVR hyperparameters may not always offer high accuracy. In this paper, we introduce a novel framework for predicting crop yields that address these challenges. Our framework integrates a new hybrid feature selection approach with an optimized SVR model to enhance prediction accuracy efficiently. The proposed framework comprises three phases: preprocessing, hybrid feature selection, and prediction phases. In preprocessing phase, data normalization is conducted, followed by an application of K-means clustering in conjunction with the correlation-based filter (CFS) to generate a reduced dataset. Subsequently, in the hybrid feature selection phase, a novel hybrid FMIG-RFE feature selection approach is proposed. Finally, the prediction phase introduces an improved variant of Crayfish Optimization Algorithm (COA), named ICOA, which is utilized to optimize the hyperparameters of SVR model thereby achieving superior prediction accuracy along with the novel hybrid feature selection approach. Several experiments are conducted to assess and evaluate the performance of the proposed framework. The results demonstrated the superior performance of the proposed framework over state-of-art approaches. Furthermore, experimental findings regarding the ICOA optimization algorithm affirm its efficacy in optimizing the hyperparameters of SVR model, thereby enhancing both prediction accuracy and computational efficiency, surpassing existing algorithms. The Author(s) 2024. -
A protoberberine alkaloid based ratiometric pH-responsive probe for the detection of diabetic ketoacidosis
Herein we report a ratiometric naturally occurring fluorescent pH probe, berberrubine (BBn) for the direct detection of diabetic ketoacidosis (DKA) conditions of patients having type I diabetes mellitus. The photophysical properties of the probe during pH titrations showed remarkable changes in absorption spectra where two absorption bands at 377 and 326 nm have disappeared followed by the emergence of an absorption maxima at 346 nm in highly acidic conditions. In addition, a fluorescence enhancement effect was observed in the alkaline pH, with a bathochromic shift of 33 nm. Moreover, the solution switches the color from light yellow to light pink with the change of pH from acidic to basic. A pKa value of 7.57 and a good linearity between pH 5.09.0 indicate that the probe can be used efficiently for the DKA condition, where pH variations are in the range of 67. The excellent water solubility, photostability, reversibility, and selectivity of BBn make it a potential pH sensing agent for acidic microenvironments. The reversible sensing of pH variations during DKA could be effective in primary detection and diagnosis which can assist in avoiding further complications of acidosis. 2021 Elsevier Ltd -
A Psychoanalytical Deconstruction of Surpanakha in Kavita Kane's Lanka's Princess
This paper endeavours to examine the character Surpanakha in Kavita Kane's novel Lanka's Princess. It attempts to critically follow her struggle in the androcentric space with the trapping of being a female. Breaking down her identity as a daughter, sister, wife and more specifically, as an individual, it tracks down the formulation of her own self-perception in order to reinterpret her femininity. Through the psychoanalytical lenses, this work also critically analyses her 'repression, rage and revenge' by connecting the dots in her journey that shape her personality. The giving of voice to the 'unvoiced' through revisionist myth making in the novel and the evolution of 'Surpanakha' from 'Meenakshi' due to her experiences in the oppressive and suffocating environment is the focal point of the paper. Keywords 2021 Copyright 2021 by Koninklijke Brill NV, Leiden, The Netherlands.