Browse Items (11810 total)
Sort by:
-
Dataset exploring organizational culture of K-12 schools
Culture can be understood as an explicit social product arising from social interaction as an intentional or unintentional consequence of behavior. Educational Institutions culture differs from other organizational cultures as it impacts teachers' performance and students' learning. In this survey the definition of organizational culture used is given by Schein, The deeper level of basic assumptions and beliefs that are, learned responses to the group's problems of survival in its external environment and its problems of internal integration; are shared by members of an organization; that operate unconsciously; and that define in a basic taken -for-granted fashion in an organization's view of itself and its environment [1]. The data contains 1158 cases collected from K-12 School teachers on their perception of values and beliefs of their organizational culture using the OCTAPACE scale. Convenience sampling is used to obtain the data from teachers. The questionnaire was administered personally to teachers from sixty-five Private aided, Private unaided and Government schools. The eight dimensions measuring values and beliefs of Educational Institutions organizational culture are Pro-action, Authenticity, Openness, Collaboration, Experimenting, Trust, Confrontation and Autonomy. Descriptive statistics are computed from the dataset. The dataset can be used by researchers for meta analysis on organizational culture and school management can explore in depth the need for an organizational culture of autonomy, experimenting, collaboration and openness among teachers. 2022 The Authors -
Influence of teacher occupational stress on self-efficacy: Evidences from the pre-and during-covid-19 periods
The teaching profession is undergoing a revolutionary change due to the compulsive integration of technology into education. There is a growing interest in understanding teachers occupational stress and efficacy in online teaching. The participants of this study were schoolteachers in Bengaluru, India. The measures from teachers occupational stress and self-efficacy scales investigate the influence of occupational stress and self-efficacy during two periods before and during COVID-19. The findings of the study reflected that the influence of occupational stress on self-efficacy doubled during COVID-19. Classroom management and resources for online teaching mainly influenced selfefficacy during COVID-19, whereas classroom management and administration proved to influence self-efficacy before COVID-19. Online learning strategies adopted by educational institutions will certainly make an impression on the education system post COVID-19. The current research contributes a significant component in the formation of teacher professional identity. 2022 IGI Global. All rights reserved. -
The role of classroom engagement on academic grit, intolerance to uncertainty and well-being among school students during the second wave of the COVID-19 pandemic in India
The forced changes and disruptions in educational systems and learning experiences due to the pandemic has impacted students' mental health and well-being. The present study aims to understand the effects of the determinants of well-being on students in India during the second wave (April to August 2021) of the COVID-19 pandemic. The determinants of well-being in this study areacademic grit, intolerance to uncertainty and students' engagement in an online learning environment. In this study, well-being is characterized as students' confidence and satisfaction in an online learning and pandemic environment. The data collected from 1174 students (1219 years) from various states, using standardized tools, were analyzed to find out about the mediating effect of students' engagement on the relationship between academic grit and well-being, and between intolerance to uncertainty and well-being. Further, the model fit analysis of the determinants of well-being is explored.The paper reports that students' classroom engagement does mediate in the path of academic grit and well-being, and in the path of intolerance to uncertainty and well-being. It also evidence the model fit of the influence of the determinants of well-being on that of school students during the second wave of the COVID-19 pandemic. The study also draws implications and suggestions for educators using the current model of students' well-being. 2022 Wiley Periodicals LLC. -
A study on the self-concept of teachers working in government, aided and unaided colleges in Bangalore
The IUP Journal of Organizational Behavior, Vol-13(1), pp. 60-70. ISSN-0972-687X -
Construction of Teachers' Personal Commitment Scale: Validity and Reliability Analysis
Indian Streams Research Journal, Vol-3(12), pp.1-6. ISSN-2230-7850 -
The influence of leadership in building a learning organization /
The IUP Journal Of Organizational Behavior, Vol.15, Issue 1, pp.7-18, ISSN: 0972-687X. -
Effectiveness of activity based program in enhancing fine motor skills of children with dyspraxia /
Scholedge International Journal Of Multidisciplinary & Allied Studies, Vol.2, Issue 5, pp.502-510, ISSN No: 2394-336X. -
Community Consciousness and The Construction of Social Honour : A Study Among The Kurds in Finland
Research on honour has recently been overshadowed by discussions surrounding honour-related violence (HRV), particularly honour killings. Typically, inquiries into how the concept of honour, often associated with violent acts, is formed, and expressed on personal or communal levels remain unanswered or are only superficially explored. This study aims to uncover the social structures underlying the concept of honour, demonstrate its manifestation through social interactions, and elucidate how individuals respond to threats against their honour. Because of the many tragic instances of HRV within Kurdish diaspora communities, Kurds and Kurdish culture frequently feature in discussions concerning this phenomenon. Therefore, data for current research has been collected from Kurds living in the diaspora, specifically in Finland. The data were elicited through a combination of methods to be as comprehensive as possible. This included 24 semi-structured interviews with individual participants and four Focus Group Discussions involving 16 participants. A closer look at the data shows that the construction of Kurdish honour is based on a complex interaction of social structures. These structures can be broken down into three key elements: institutional, relational, and embodied factors. In this regard, the research has identified six structural components. The study demonstrates that the intensity of honour-related conflicts stems from an intricate interplay of structural factors. However, the relative weight of these factors determines the flashpoints of tension. Through the lens of structuration theory, the study demonstrates the role of everyday practices and dynamic social factors in shaping individuals' discursive consciousness around honour. Individuals' conceptualization of honour may sometimes diverge from its practical application, leading to a space for social negotiation where honour undergoes continuous reshaping into a dynamic equilibrium that impacts daily life. It's essential to recognize that this equilibrium varies among individuals. However, a threat to honour can prompt varying facework procedures, contingent upon individual characteristics and community expectations. Current Inquiry uncovers six distinct behavioural patterns individuals exhibit during a crisis of honour. The investigation of the participants' narratives reveals a diverse Kurdish culture, as evidenced by their different perceptions about the tolerable thresholds of honour violations and contradictory discourses concerning honour. The final significant finding is that the fear of dishonour typically outweighs the desire for honour among the Kurds, as indicated by both daily language use and chronological analysis of conflict cases. This observation holds significant implications, not only for reevaluating normative terminology but also for informing social and political preventive measures. -
A review on extraction and separation of cellulose fibers from agro wastes
Over the past few decades, there was significant increase in research concerning resources that have certain desirable characteristics like renewability, ease of availability, economic value, excellent mechanicalthermal properties, biocompatibility and biodegradability. Cellulose is one such resource that possesses these characteristics and yet various sources that constitute ample quantities of lignocellulose are discarded, as their peculiarities and applications were not widely known to the population. Agro wastes, which are generated every year at a tremendous rate, are viewed as a promising substrate for the commercial extraction of cellulose. Hence in this review, an appropriate utilization of these agricultural by-products, with respect to extraction of cellulose is discussed, so as to ameliorate their applications in an aim to diminish the disposal rate of essential commodities. 2021 World Research Association. All rights reserved. -
A Review of Historical Context and Current Research on Cannabis Use in India
Background: The cultivation and use of cannabis is historically rooted in the Indian subcontinent and this rich heritage of cannabis use dates back to at least two thousand years. Cannabis remains an illicit substance in India despite its changing status globally with many countries legalizing cannabis use in recent years. Scientific research on cannabis use in India has also been sparse. Method: Extensive search of online databases resulted in the identification of 29 original research studies pertaining to one of three areas of cannabis research; a) prevalence of cannabis use b) psychological correlates of cannabis use, c) cannabis use in substance use treatment settings. Findings: We found that most Indian studies used very basic quantitative research designs and had poor scientific rigor. Samples were small, region specific and included only males. Data analyses were limited to descriptive methods. The criteria for cannabis use in most of the reviewed studies were not rigorous and prone to biases. Conclusion & Implications: With changing attitudes and loosening of restrictions on cannabis use, the prevalence of new users is rising dramatically particularly in the college going population. This presents a strong need for research on motivations and attitudes to cannabis use and how those can influence patterns of use, and also the short- and long-term effects of use. More studies with stronger research designs (both cross sectional and longitudinal) are required for the study of cannabis use and this knowledge will be critical for managing the growing substance epidemic, generating public health solutions as well as formulating effective policy frameworks. 2022 The Author(s). -
Enhanced Automated Oxygen Level controller for COVID Patient By Using Internet of Things (IoT)
The Internet of Things (IoT) shall be merged firmly and interact with a higher number of altered embedded sensor networks. It provides open access for the subsets of information for humankind's future aspects and on-going pandemic situations. It has changed the way of living wirelessly, with high involvement and COVID-related issues that COVID patients are facing. There is much research going on in the recent domain, like the Internet of Things. Considering the financial-economic growth, there isn't much significance as IoT is growing with industry 5.0 as the latest version. The newly spreading COVID-19 (Coronavirus Disease, 2019) will emphasize the IoT based technologies in a greater impact. It is growing with an increase in productivity. In collaboration with Cloud computing, it shows wireless communication efficiently and makes the COVID-19 eradication in a greater way. The COVID-19 issues which are faced by the COVID patients. Many patients are suffering from inhalation because of lung problems. The second wave attacks mainly on the lungs, where there is a shortage of breathing problems because of less supply of oxygen (insufficient amount of oxygen). The challenges emphasized as proposed are like the shortage of monitoring the on-going process. Readily being active in this pandemic situation, the mentioned areas are from which need to be discussed. The frameworks and services are given the correct data and information for supply of oxygen to the COVID patients to an extent. The Internet of Things also analyzes the data from the user perspective, which will later be executed for making on-demand technology more reliable. The outcome for the COVID-19 has been taken completely to help the on-going COVID patients live, which can be monitored through Oxygen Concentration based on the IoT framework. Finally, this article discusses and mentions all the parameters for COVID patients with complete information based on IoT. 2022 IEEE. -
Cloud Computing Application: Research Challenges and Opportunity
In a world with intensive computational services and require optimal solutions, cloud security is a critical concern. As a known fact, the cloud is a diverse field in which data is crucial, and as a result, it invites the dark world to enter and create a virtual menace to businesses, governments, and technology that is facilitated by the cloud. This article addresses the fundamentals of cloud computing, as well as security and threats in various applications. This research study will explore how security is remaining as a potential risk for cloud users across the globe by listing some of the cloud applications. Some viable solutions and security measures that could help us in analyzing cloud security threats are reviewed. The analyzed solutions include profound analytical thinking on how to render the solutions more impactful in each scenario. Several cloud security solutions are available to assist businesses in reducing costs and enhancing security. This study discover that if the risks are taken into consideration without any delay then the matter of solutions gets divided into four pillars, which will assist us in obtaining a more comprehensive knowledge. Visibility, compute-based security, network protection, and lastly identity security are referred as four pillars. 2022 IEEE. -
Anomalous indirect carrier relaxation in direct band gap atomically thin gallium telluride
We report ultrafast studies on atomically thin Gallium telluride, a 2D metal monochalcogenide that has appeared to display superior photodetection properties in visible frequencies. Pump photon energy-dependent spectroscopic studies reveal that photoinduced carriers in this direct band-gap material undergo indirect relaxation within ?30 ps of photoexcitation, which is at least an order slower than that of most 2D materials. Despite the direct band-gap nature, slow and indirect carrier relaxation places this layered material as a prime candidate in the multitude of atomically thin semiconductor-based photodetectors and highlights the potential for prospective optoelectronic applications. 2023 American Physical Society. -
Enhanced Sensing Performance of an Ammonia Gas Sensor Based on Ag-Decorated ZnO Nanorods / Polyaniline Nanocomposite
The development of low-cost ammonia sensors with high sensitivity and selectivity has gained considerable interest. Though the response of these sensors at room temperature is low and needs enhancement. In the present study high sensitivity ammonia gas sensors based on nanocomposite films of polyaniline (PANI) and with varying ZnO concentrations were synthesized and investigated. With a loading of 10 at% ZnO, the gas sensing response of 59 % was obtained for 120 ppm NH3 gas. The gas response was further enhanced by decorating the ZnO nanorods with different concentrations of silver (Ag) nanoparticles. The Ag-decorated ZnO nanorods were embedded in the PANi matrix using the in-situ oxidative polymerization technique. It was shown that PANi ZnO, p-n junction, and the introduction of porosity in nanocomposite act synergistically in increasing the resistance caused by the deprotonation of PANi by NH3. Among various compositions studied, 2 % loading of Ag in ZnO embedded in PANi matrix, thin films were found to be highly selective and sensitive towards NH3 gas at room temperature with a chemiresistive response of 70 % at 120 ppm and a recovery time of less than 120 s. The selectivity of the nanocomposite was also studied towards various reducing and oxidizing gasses. 2023 Wiley-VCH GmbH. -
Deterministic, Stochastic, and Deep Learning Approaches to Understand the Economic Fluctuations in India
In the present work, a new mathematical framework is proposed for studying the interrelation among population growth rate, GDP, inflation rate, and unemployment rate within deterministic and stochastic frameworks. The values of the parameters of the proposed model are estimated using real data from India. The local and global uniqueness of solutions is established for the stochastic model. The deterministic model is solved by using the Adams-Bashforth-Moulton predictor-corrector method, and Milstein's method is used for solving the stochastic model. Numerical simulations correlated quite strongly with observed data, while projections for the 20242030 period indicate that controlled population growth bodes well for the outlook of the economy for India, supporting economic prosperity alongside reduced inflation and better employment conditions. The findings presented in this work are correlational; therefore, to find the possible cause for this phenomenon, further research is required with detailed datasets. Comparing our model's GDP predictions with that obtained using a long short-term memory recurrent neural network model returned very high values of predictive accuracy, thus reinforcing the strength and reliability of our framework. 2025 John Wiley & Sons Ltd. -
Diabetes Mellitus Classification Using Machine Learning Algorithms with Hyperparameter Tuning
Diabetes Mellitus is a prevalent condition globally, marked by elevated blood sugar levels resulting from either insufficient production of insulin or the body cells' inability to respond appropriately to released insulin. For people with diabetes to lead healthy, normal lives, early identification and treatment of the condition are essential. With the need to move away from current traditional procedures, towards a noninvasive methodology, machine learning and data mining technologies can be very useful in the classification of diabetes. Creating an effective machine learning model for the classification of diabetes mellitus was the primary goal of this research. This work is primarily carried out on combined Pima Indian diabetes dataset and German Frankfurt diabetes dataset. The class imbalance issue has been resolved using Synthetic Minority Oversampling Technique. One-hot encoding is applied to convert categorial features to numerical and various single and ensemble classifiers with the best hyperparameters obtained using GridSearchCV method were employed on the pre-processed dataset. With an AUC of 0.98 and maximum accuracy of 98.79%, the Random Forest ensemble technique outperformed the other models, according to the experimental results. As a result, the algorithm might be used to predict diabetes and alert doctors to serious cases that call for emergency care. 2024 IEEE. -
A Comprehensive Review on Heart Disease Risk Prediction using Machine Learning and Deep Learning Algorithms
Cardiovascular diseases claim approximately 17.9 million lives annually, with heart attacks and strokes accounting for over 80% of these deaths. Key risk factors, including hypertension, hyperglycemia, dyslipidemia, and obesity, are identifiable, offering opportunities for timely intervention and reduced mortality. Early detection of heart disease enables individuals to adopt lifestyle changes or seek medical treatment. However, conventional diagnostic methods, such as electrocardiogramscommonly used in clinics and hospitals to detect abnormal heart rhythmsare not effective in identifying actual heart attacks. Additionally, angiography, while more precise, is an invasive method, financial strain on patients, and high chances of incorrect diagnosis, highlighting the need for alternative approaches. The main goal of this study was to assess the accuracy of machine learning techniques, including both individual and combined classifiers, in early detection of heart diseases. Furthermore, the study aims to highlight areas where additional research is necessary. Our investigation covers a decade period from 2014 to 2024, including a thorough review of pertinent literature from international conferences and top journals from the databases like Springer, ScienceDirect, IEEEXplore, Web of Science, PubMed, MDPI, Hindawi and so on. The following keywords were used to search the articles: heart disease risk, heart disease prediction, data mining, data preprocessing, machine learning algorithms, ensemble classifiers, deep learning algorithms, feature selection, hyperparameter optimization techniques. We examine the methodologies used and evaluate their effectiveness in predicting cardiovascular conditions. Our findings reveal notable progress in applying machine learning and deep learning in cardiology. The study concludes by proposing a framework that incorporates current machine learning techniques to enhance heart disease prediction. The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2024. -
Hybrid Deep Learning Cloud Intrusion Detection
The scalability and flexibility that cloud computing provides, organisations can readily adapt their resources to meet demand without having to make significant upfront expenditures in hardware infrastructure. Three main types of computing services are provided to people worldwide via the Internet. Increased performance and resource access are two benefits that come with using cloud computing, but there is also an increased chance of attack. As a result of this research, intrusion detection systems that can process massive amounts of data packets, analyse them, and produce reports using knowledge and behaviour analysis were created. Convolution Neural Network Algorithm encrypts data as it's being transmitted end-to-end and is stored in the cloud, providing an extra degree of security. Data protection in the cloud is improved by intrusion detection. This study uses a model to show how data is encrypted and decrypted, of an algorithm and describes the defences against attacks. When assessing the performance of the suggested system, it's critical to consider the time and memory needed to encrypt and decrypt big text files. Additionally, the security of the cloud has been investigated and contrasted with various encoding techniques now in use. 2024 IEEE. -
Investigation of Brain Tumor Recognition and Classification using Deep Learning in Medical Image Processing
A brain tumour is the growth of brain cells that are abnormal, some of which may progress into cancer. Magnetic Resonance Imaging (MRI) scans are the method used most frequently to detect brain tumours. The brain's abnormal tissue growth can be seen on the MRI images, which reveal. Deep learning and machine learning techniques are employed to identify brain tumours in a number of research publications. It only takes a very short amount of time to predict a brain tumour when these algorithms are applied to MRI images, and the increased accuracy makes patient treatment simpler. Thanks to these forecasts, the radiologist can make quick decisions. The suggested approach employs deep learning, a convolution neural network (CNN), an artificial neural network (ANN), a self-defined neural network, andthe existence of brain tumor. 2022 IEEE. -
A Shortest Path Problem for Drug Delivery Using Domination and Eccentricity
The concept of domination was first introduced in by Ore in 1962. With this, the study of domination gained importance and has been vigorously studied since then. The idea about eccentricity for vertices in a graph was given by Buckley and Harary in 1990. This paper combined the ideas about domination and eccentricity and provides the observation obtained during the study. Most of the basic ideas about domination and eccentricity has been covered and also a comparative study between these two has been stated along with problem of drug transportation through networks. These ideas can be further used to solve the real-world problems which uses concepts of domination and eccentricity like for example drug delivery game theory problems, routing problem, assignment problem and many more. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.