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An efficient deep learning based stress monitoring model through wearable devices for health care applications
Due to the mental stress of the human, the negative effects are known to be recent decades. Early detections of high level stresses are necessary to stop harmful consequences. Studies have proposed on wearable technologies which detect human stress. This study proposes stress detection systems which use physiological signals of people collected by wearable technologies and attached to human bodies. They can carry it during their daily routine. This works proposed system includes removal of artifacts in bio signals and feature extractions from these cleaned signals. Since, DL (deep learning) based models are proven to be the best for these analyses, this article uses a random differential GWO (Grey wolf optimization) algorithm for feature extraction and a ML (machine learning) algorithm called RF (random forest) has been used for classification of the human body parameters like activities of the heart, conductance in skins and corresponding accelerometer signals. The proposed stress detection system is implemented with the real time data gathered from 21 participants. This approach can detect the stress of a human and prevent it from early stages with necessary lectures to avoid the negative effects. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Social media strategies of royal challenges Bangalore team and perception of its fans /
Indian Premier League (IPL) buzz is primarily driven by marketing actions of the broadcaster, Here the researcher brings out the key aspects for the success of Royal Challengers Bangalore (RCB) franchise through social media part of sports communication to connect with their fans to build a brand identity, manage reputations, and engage fans through various clubs. -
Gray Level Co-occurrence Matrix based Fully Convolutional Neural Network Model for Pneumonia Detection
This study presents a new method to improve the detection ability of a convolutional neural network (CNN) in pneumonia detection using chest X-ray images. Using Gray-Level Co-occurrence Matrix (GLCM) analysis, additional channels are added to the original image data provided by Guangzhou Children's Hospital in Guangzhou, China. The main goal is to design a lightweight, fully convolution network and increase its available information using GLCM. Performance analysis is performed on the new CNN model and GLCM-enhanced CNN model, and results are compared with Transfer Learning approaches. Various evaluation metrics, including accuracy, precision, recall, F1 score, and AUC-ROC, are used to evaluate the improved analysis performance of CNN. The results showed a significant increase in the ability of the model to detect pneumonia, with an accuracy of 99.57%. In addition, the study evaluates the descriptive properties of the CNN model by analyzing its decision process using Grad-CAM. 2024, J.J. Strossmayer University of Osijek, Faculty of Electrical Engineering, Computer Science and Information Technology. All rights reserved. -
DEVELOPMENT AND EVALUATION OF PNEUMFC NET: A NOVEL AUTOMATED LIGHTWEIGHT FULLY CONVOLUTIONAL NEURAL NETWORK MODEL FOR PNEUMONIA DETECTION
The aim of this study is to address the challenges of pneumonia diagnosis under constraint resources and the need for quick decision making. We present the PneumFC Net, a novel architectural solution where our approach focuses on minimizing the number of trainable parameters by incorporating transition blocks that efficiently manage channel dimensions and reduce number of channels. In contrast to using fully connected layers, which disregard the spatial structure of feature maps and substantially increase parameter counts, we exclusively employ only convolutional layer approach. In the study, X-ray image dataset is used to train and evaluate the proposed Convolutional Neural Network model. By carefully designing the architecture, the model achieves a balance between parameters and accuracy while maintaining comparable performance to pre-trained models. The results demonstrate the model's effectiveness in detecting pneumonia images reliably. In addition, the study examines the decision-making process of the model using Grad-CAM, which helps to identify important aspects of radiographic images that contribute to the positive pneumonia prediction. Furthermore, the study shows that the proposed model, Pneum FC Net not only has the highest accuracy of 98%, but the total trainable model parameters is only 0.02% of the next best model VGG-16, thus establishing the potential of this new robust Deep Learning model. This research primarily addresses concerns related to mitigating significant computational requirements, with a specific focus on implementing lightweight networks. The contribution of this work involves the development of resource-efficient and scalable solution for pneumonia detection. 2024 Little Lion Scientific. All rights reserved. -
Comparison of Augmentation and Preprocessing Techniques for Improved Generalization Performance in Deep Learning based Chest X-Ray Classification
Convolutional Neural Network (CNN) models are well known for image classification; however, the downside of CNN is the ineffectiveness to generalize and inclination towards over-fitting in case of a small train dataset. A balanced and sufficient data is thus essential to effectively train a CNN model, but this is not always possible, especially in the case of medical imaging data, as often patients with the same disease are not always available. Image augmentation addresses the given issue by creating new data points artificially with slight modifications. This study, investigates ten different methods with various parameters and probability and their combined effect on the test dataset's generalization performance and F1 Score. For the study, three pre-Trained CNN models, namely ResNetl8, ResNet34, and ResNet50, are fine tuned on a small training dataset of 500 Pneumonia and 160 Non-Pneumonia(Normal) Images for each augmentation setting. The test accuracy, F1 Score, and generalization performance were calculated for a test dataset consisting of 50 Pneumonia and 16 Non-Pneumonia(Normal) Images. 2022 IEEE. -
Detection and Classification of Thoracic Diseases in Medical Images using Artificial Intelligence Techniques: A Systematic Review
Artificial Intelligence is at the leading edge of innovation and is developing very fast. In recent studies, it has played a progressive and vital role in Computer-Aided Diagnosis. The chest is one of the large body parts of human anatomy and contains several vital organs inside the thoracic cavity. Furthermore, chest radiographs are the most commonly ordered and globally used by physicians for diagnosis. An automated, fast, and reliable detection of diseases based on chest radiography can be a critical step in radiology workflow. This study presents the conduction and results of a systematic review investigating Artificial Intelligence Techniques to identify Thoracic Diseases in Medical Images. The systematic review was performed according to PRISMA guidelines. The research articles published in English were filtered based on defined inclusion and exclusion criteria. The Electrochemical Society -
Nutraceuticals to prevent and manage cardiovascular diseases
Unhealthy lifestyle and diet are the key risk factors to cardiovascular diseases. A healthy cardiac and vascular system can prevent cardiovascular-related diseases like hypertension, atherosclerosis, and heart stroke. Identifying pharmacologically important metabolites had paved the way to contemporary medicine. People are more attracted to them as they are majorly plant-based metabolites such as polysaccharides, polyphenols, polysterols, and vitamins as cardio-protectors. Preclinical, clinical, and animal studies provide substantial data confirming nutraceuticals as a promising therapeutic agent in curing cardiovascular diseases. This chapter summarizes on major bioactive molecules as nutraceuticals with preclinical and clinical studies, emphasising their cardiovascular protective roles. 2023 Elsevier Inc. All rights reserved. -
A critical analysis of the law relating to elementary education in India viz-a-viz minimum level of learning /
The “demand for free and compulsory education” started in the British period. The framers directed that the object of Article 45 must be achieved within ten years of the commencement of the constitution. After several judicial strictures the Constitutional (Eighty Sixth) Amendment Act 2002 (Amendment Act, 2002) declared ‘education’ as a fundamental right. -
A Critical analysis of the law relationg to elementary education in india Viz-A-Viz Minimum level of learning
The demand for free and compulsory education started in the British period. newlineThe framers directed that the object of Article 45 must be achieved within ten years newlineof the commencement of the constitution. After several judicial strictures the newlineConstitutional (Eighty Sixth) Amendment Act 2002 (Amendment Act, 2002) declared education as a fundamental right. To execute this mandate the Children Right to Free and Compulsory Education Act 2009 (RTE Act, 2009) was passed. However, the government has failed to fulfil the mandate of giving quality education in terms of minimum level of learning under the RTE Act. One of the newlineparameters of the Quality Monitoring Tool under Sarva Shiksha Abhiyan (SSA) is newlineto give quality education in terms of minimum level of learning. But, when it was newlinerealized that the children from Government schools are completing their primary newlineeducation without minimum level of learning, three amendments had been made to the RTE Act 2009, i.e., during 2012, 2015 and 2019. Yet, the latest Annual Survey on Education Report shows that there is poor minimum level of learning among children who have completed their primary education in government primary newlineschools. Thus, the purpose of this research is to critically analyse the RTE Act 2009 newlineand the subsequent amendments to the Act of 2009 and to study various policies and schemes under SSA, to explore the reasons for low level of learning in government primary school and to make suggestion to improve the minimum level of learning. -
Static perfect fluid space-Time and paracontact metric geometry
The main purpose of this paper is to study and explore some characteristics of static perfect fluid space-Time on paracontact metric manifolds. First, we show that if a K-paracontact manifold M2n+1 is the spatial factor of a static perfect fluid space-Time, then M2n+1 is of constant scalar curvature-2n(2n + 1) and squared norm of the Ricci operator is given by 4n2(2n + 1). Next, we prove that if a (?,?)-paracontact metric manifold M2n+1 with ? >-1 is a spatial factor of static perfect space-Time, then for n = 1, M2n+1 is flat, and for n > 1, M2n+1 is locally isometric to the product of a flat (n + 1)-dimensional manifold and an n-dimensional manifold of constant negative curvature-4. Further, we prove that if a paracontact metric 3-manifold M3 with Q? = ?Q is a spatial factor of static perfect space-Time, then M3 is an Einstein manifold. Finally, a suitable example has been constructed to show the existence of static perfect fluid space-Time on paracontact metric manifold. 2022 World Scientific Publishing Company. -
The zamkovoy canonical paracontact connection on a para-kenmotsu manifold
The object of the paper is to study a type of canonical linear connection, called the Zamkovoy canonical paracontact connection on a para-Kenmotsu manifold. 2021 D. G. Prakasha et al. -
On (?)-Lorentzian para-Sasakian Manifolds
The object of this paper is to study (?)-Lorentzian para-Sasakian manifolds. Some typical identities for the curvature tensor and the Ricci tensor of (?)-Lorentzian para-Sasakian manifold are investi-gated. Further, we study globally ?-Ricci symmetric and weakly ?-Ricci symmetric (?)-Lorentzian para-Sasakian manifolds and obtain interesting results. 2022 Academic Center for Education, Culture and Research TMU. -
h-almost Ricci Solitons on Generalized Sasakian-space-forms
The aim of this article is to study the h-almost Ricci solitons and h-almost gradient Ricci solitons on generalized Sasakian-space-forms. First, we consider h-almost Ricci soliton with the potential vector field V as a contact vector field on generalized Sasakian-space-form of dimension greater than three. Next, we study h-almost gradient Ricci solitons on a three-dimensional quasi-Sasakian generalized Sasakian-space-form. In both the cases, several interesting results are obtained Kyungpook Mathematical Journal -
Invariant Submanifolds of (?)-Sasakian Manifolds
In this paper, we consider invariant submanifolds of an (?)-Sasakian manifolds. We show that if the second fundamental form of an invariant submanifold of a (?)-Sasakian manifold is recurrent then the submanifold is totally geodesic. We also prove that, invariant submanifolds of an Einstein (?)-Sasakian manifolds satisfying the conditions (Formula presented) (X, Y) ? = 0 and (Formula presented)(X, Y) (Formula presented)? = 0 with ?r ? n(n 1) are also totally geodesic. 2020. All Rights Reserved. -
A Novel Approach for Fractional (1 + 1 ) -Dimensional BiswasMilovic Equation
In this paper, we find the solution for (1 + 1 ) -dimensional fractional Biswas-Milovic (FBM) equation using the q-homotopy analysis transform method (q-HATM). The Biswas-Milovic equation is a generalization of the nonlinear Schringer (NLS) equation. The future scheme is the elegant mixture of q-homotopy analysis scheme with Laplace transform technique and fractional derivative considered in Caputo sense. To validate and illustrate the competence of the method, we examine the projected model in terms of arbitrary order. Moreover, the nature of the attained results have been presented in 3D plots and contour plots for different value of the order. The gained consequences show that, the hired algorithm is highly accurate, easy to implement, and very operative to investigate the nature of complex nonlinear models ascended in science and engineering. 2021, The Author(s), under exclusive licence to Springer Nature India Private Limited. -
ON 3-DIMENSIONAL QUASI-PARA-SASAKIAN MANIFOLDS AND RICCI SOLITONS
The purpose of this paper is to study 3-dimensional quasi-para-Sasakian manifolds and Ricci solitons. First, we prove that a 3-dimensional non-paracosymplectic quasi-para-Sasakian manifold is an ?-Einstein manifold if and only if the structure function ? is constant. Further, it is shown that a Ricci soliton on a 3-dimensional quasi-para-Sasakian manifold with ?=constant is expanding. Moreover, we show that if a 3-dimensional quasi-para-Sasakian manifold admits a Ricci soliton, then the flow vector field V is Killing, and the quasi-para-Sasakian structure can be obtained by a homothetic deformation of a para-Sasakian structure. Besides, we study gradient Ricci solitons and prove that if a 3-dimensional non-paracosymplectic quasi-para-Sasakian manifold with ?=constant admits a gradient Ricci soliton, then the manifold is an Einstein one. Also, a suitable example of a 3-dimensional quasi-para-Sasakian manifold is constructed to verify our results. 2022 A. Razmadze Mathematical Institute of Iv. Javakhishvili Tbilisi State University. All rights reserved. -
Professional Ethics of Teachers in Educational Institutions
Artha Journal of Social Sciences, Vol-11 (4), pp. 24-32. ISSN-0975-329X -
Construction and validation of next generation teacher educator competency scale [NGTECS]
The article presented here is a part of the major research project conducted in India from 2016 through 2020. Teacher education in India in the last six decades underwent many reforms in terms of policies, assessment, field experience patterns, and training pedagogy. Teacher educators invariably implemented all the suggested changes. However, it lacked the rigour in implementation. At the same time with the advancement of technology, the student population is native to technology and had newer attitudes towards learning. Researcher felt that, the Teacher educators competencies are to be revisited and may come up with a new list of competencies which are helpful to the next generation. It is with this intent this major research project was planned. The purpose of the project was to come up with the list of competencies for teacher educators which are helpful for training the next generation teacher trainees and to measure those competencies. Therefore, the study resorted to adopt exploratory research design. This article explains the step wise procedure on construction of next generation teacher educator competency scale. It also explains the procedures of validity and reliability established for the scale. The study brought out a Likert type of scale to measure next generation teacher educator competency and has simple administration procedures. The scale was found to be highly reliable based on Cronbach's alpha internal consistency coefficient. Though the study is exploratory in nature, yet confirmatory factor analysis was carried out and is found to be in agreement with the evolved factors. The scale may be used to measure the extent to which the next generation teacher educator competencies are possessed by a teacher educator. Copyright 2020 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License -
exploring effective Online-Teaching Transition of College Teachers During COVID-19
The study attempts to identify what is effective online teaching from teacher and student perspectives. What are the challenges faced by teachers which hampered effective online teaching? The study employed mixed method research design including a survey questionnaire and semi-structured interview. The study collected data from 500 students out of which 200 are boys and 300 are girls on effective online teaching. The study conducted semi-structured interview with eight college teachers through snowball sampling. The survey revealed that almost 80% of the teachers are not effective. Girls are less satisfied with online teaching transition of teachers than boys are. Similarly, postgraduates (PG) are not as satisfied as undergraduate (UG) students are. Interview data revealed themes and subthemes on challenges of effective online teaching faced by college teachers. Overall, the perceived online-teaching effectiveness is low, and further research may find the causes for the same. 2022 IGI Global. All rights reserved. -
Energy saving, waste management, and pollution free steps for university campuses
Global warming is a worldwide concern and the documents related to the need for sustainable measures seen in academic and non-academic literature. In a highly populated country like India, these are more severe worries. Multiple established educational institutions across India have taken significant steps in educating their students on sustainable development goals (SDG). Currently there is a need to assess the extent of effect such training has on student populations of such institutes. Present study attempted to assess the efficacy of SDG-implementation training programmes in a reputed private university, through student assessment of student behaviors outside the institute and in their personal life. Using semi-structured in-depth interview methods, interviewed eight students of Undergraduate and Postgraduate programmes. These students were active participants of community service programmes arranged by the university within a sustainable development model. Data were analyzed using reflexive thematic analysis methods. Emerged themes from data analysis indicate a positive change in their worldview and significant modifications in their personal behavior towards sustainability because of being part of such programmes. Educating others through practice and increased socio-environmental awareness were also major themes. Current study contributes in assessing efficacy of sustainability programmes in educational institutions. This study also suggests few recommendations for increasing competence of the same. 2021 Author(s).