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Stakeholders' pedagogical preferences for teaching 'marketing' in management education
This study has been realized that there is a dire need for re-thinking, particularly obvious for matters of assessment and its relation to the current focus on teaching marketing. A descriptive design of the research was used where convenient sampling has been followed for data collection. In order to achieve the purpose, it was decided to collect independent opinions of students, teachers, and professionals. Analysis has been done through descriptive statistics and Spearman's rank correlation. As a result, a significant difference between the stakeholders' perceptions about the pedagogy for teaching marketing in management education was identified. 2021 Ecological Society of India. All rights reserved. -
Occupational stress: A pre and post COVID-19 perspective on teaching personnel in higher education institutions of India
The COVID-19 as a catalytic phenomenon exposes many loop holes in the socio-economic sustainability of business and society. It exposed people to different occupational stressors and anxiety. This study was focused on occupational stress of teaching personnel in higher education institutions (HEIs), in both pre and post COVID-19 scenario. Descriptive analysis shows work autonomy, career progression, community membership, work conditions, and freedom to use own judgements are major stressors to to HEI teachers in the post COVID-19 scenario. Inferential evaluation has confirmed that Job security, social service, and creativity are major concerns to HEI teachers. They experience limitations to try their own ways of doing job. 2021 Ecological Society of India. All rights reserved. -
Comparative Analysis of Machine Learning Models in Predicting Academic Outcomes: Insights and Implications for Educational Data Analytics
In the evolving landscape of educational research, the predictive analysis of student performance using data science has garnered significant interest. This study investigates the influence of diverse factors on academic outcomes, ranging from personal demographics to socioeconomic conditions, to enhance educational strategies and support mechanisms. We employed a diverse ml models to analyze a information containing academic records and socioeconomic information. The models tested include Logistic Regression, Random Forest (RF), Gradient Boosting (GB), Support Vector Machines (SVC), K-Nearest Neighbors (KNN), Gaussian Naive Bayes, and Decision Trees. The process involved comprehensive data preprocessing, exploratory analysis, model training, and evaluation based on metrics such as precision, recall, accuracy, and F1 score. The results indicate that ensemble methods, specifically RF and GB, demonstrate superior efficacy in accurately predicting categories of student performance such as 'Enrolled,' 'Graduated,' and 'Dropped Out.' These models excelled in handling the complex interplay of varied predictors affecting student success. The results further underline the potential of advanced ensemble ML techniques in significantly outperforming the prediction accuracy in the academic domain, hence facilitating the tailoring of educational interventions to foster improved engagement and better outcomes for students. This has provided a comparative analysis of the methods that guide the future application of predictive analytics in education. 2024 IEEE. -
Towards an Improved Model for Stability Score Prediction: Harnessing Machine Learning in National Stability Forecasting
In our increasingly interconnected world, national stability holds immense significance, impacting global economics, politics, and security. This study leverages machine learning to forecast stability scores, essential for understanding the intricate dynamics of country stability. By evaluating various regression models, our research aims to identify the most effective methods for predicting these scores, thus deepening our insight into the determinants of national stability. The field of machine learning has seen remarkable progress, with regression models ranging from conventional Linear Regression (LR) to more complex algorithms like Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting (GB). Each model has distinct strengths and weaknesses, necessitating a comparative analysis to determine the most suitable model for specific predictive tasks. Our methodology involves a comparative examination of models such as LR, Polynomial Regression (PR), Lasso, Ridge, Elastic Net (ENR), Decision Tree (DT), RF, GB, K-Nearest Neighbors (KNN), and SVR. Performance metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared (R2) assess each model's predictive accuracy using a diverse dataset of country stability indicators. This study's comprehensive model comparison adds novelty to predictive analytics literature. Our findings reveal significant variations in the performance of different regression models, with certain models exhibiting exceptional predictive accuracy, as indicated by high R2 values and low error metrics. Notably, models such as LR, SVR, and Elastic Net demonstrate outstanding performance, suggesting their suitability for stability score prediction. 2024 IEEE. -
Prediction of Stock Prices using Prophet Model with Hyperparameters tuning
As part of the data analytical process, predicting and time - series are crucial. In academics and financial research, anticipating share prices is a prominent and significant subject. A share market would be an uncontrolled environment for anticipating shares since there are no fundamental guidelines for evaluating or anticipating share prices there. As a result, forecasting share prices is a difficult time-series issue. fundamental, technical, time series predictions and analytical strategies are just a few of the various techniques and approaches that machine learning uses to execute stock value predictions. This article implements the stock price prediction, Researchers compared the model of the prophet with the tuned model of the prophet. By utilizing the tuning of hyperparameters using parameter grid search to improve the performance of the model accuracy for the best prediction. The findings of the study demonstrated that tuned model of the prophet with hyperparameters tuning which results in model accuracy and based on the experimental findings mean squared error (MSE) and mean absolute percentage error (MAPE) has significant improvement. 2022 IEEE. -
PEVRM: Probabilistic Evolution Based Version Recommendation Model for Mobile Applications
Traditional recommendation approaches for the mobile Apps basically depend on the Apps related features. Now a days many users are in quench of Apps recommendation based on the version description. Earlier mobile Apps recommendation system do not handle the cold start problem and also lacks in time for recommending the related and latest version of Apps. To overcome this issues, a hybrid Apps recommendation framework which is considering the version of the mobile Apps is proposed. This novel framework named 'Probabilistic Evolution based Version Recommendation Model (PEVRM)' integrates the principles of Probabilistic Matrix Factorization (PMF) with Version Evolution Progress Model (VEPM). With the help this novel recommendation algorithm, the mobile users easily identify the specific Apps for particular task based on its version progression. At same time, this framework helps in resolving cold start problems of new users. Evaluations of this framework utilize a benchmark dataset, i.e., Apple's iTunes App Store3, for revealing its promising performance. 2013 IEEE. -
Empowering Solar Power Generation: The Z-Source Inverter Approach
The Z-source inverter model is a revolutionary design obtainable in this study for solar power conversion systems that do away with the traditional intermediary DC/DC converter. The competitive pricing of renewable energy bases in the market has drawn a percentage of attention in recent times. Government funding and technological advancements are to blame. Astral photovoltaic system is a created green technology that requires no upkeep, requires less time to install, and has grid parity. Systems for solar PV supply are categorized based on the phases of conversion. In order to maximize the amount of power created by solar energy, the conventional boost converter is utilized as an intermediary power conversion circuit. Voltage source inverters, or VSIs, are frequently employed to provide a controlled AC voltage at the output. However, the inability of VSIs to control current properly results in overcurrent problems during fault conditions. The size, weight, and switching losses of the filter circuit are condensed by the suggested converter. To solve the aforementioned issues, a Z-source inverter (ZSI) is replaced as an alternative of the voltage source inverter (VSI) in variable speed drive systems. One type of single-stage buck-boost inverter is the Z-source inverter. It functions similarly to a conventional VSI in buck mode, with six active vectors, and adds an additional switching state in boost mode, known as the shoot-through state, through utilizing a resistance Z-network. The resistivity network is regarded as an appealing solution for a number of applications since it raises the DC link current to the necessary level. In comparison to the traditional two-stage influence adaptation, the developed Z-source inverter extracts greater power from photovoltaic arrays. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Leadership management strategies and organisational practices with respect to the hotel sector of rainbow tourism group limited
This was a research exercise which sought to explore leadership management strategies and organisational practices taking place in the hotel sector of Rainbow Tourism Group Limited (RTGL). RTGL is one of Zimbabwe s biggest hotel and tourism sector. The hotel portfolio are comprised of Rainbow Towers and Conference Centre, Bulawayo Rainbow, Victoria Falls Rainbow, Kadoma Rainbow and Conference Centre, A Zambezi River Lodge and Ambassador Hotel. The six hotels have a combined total of 886 rooms, with the largest number of 304 rooms being in the five star hotel, Rainbow Towers and Conference Centre and while the rest in the three star group hotels. Other operations outside Zimbabwe are Hotel Edinburg, the Savoy Hotel and a hotel in Mozambique. The problem statement of the research study was to examine the role of organisational culture in shaping the leadership strategies in hotel and catering sector, organizational leadership and their effectiveness in helping to achieve organizational objectives. The following was newlinethe set of objectives that the research sought to achieve. Firstly, the research sought to determine and analyze the different types of leadership strategies adapted by the hotel sector of Rainbow Tourism Group Limited in the hotel sector a Case Study. The second newlineobjective was to determine the environment forces affecting the acceptance and newlineassimilation of the mentioned strategies. Thirdly the research sought to ascertain the newlineefficacy of mentioned strategies in attaining these organisational strategies in the hotel newlinesector of RTGL. The fourth objective was to suggest if any alternate strategies will be newlinerequired to enhance leadership effectiveness in Hotel Sector of the RTGL. Finally the newlineresearch sought to develop a Leadership Model that can be used in the hotel industry. -
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The Impact of Helicobacter Pylori Infection on Renal and Hepatic Function Parameters in Saudi Arabia, A Case-Control Study
Helicobacter pylori (H. pylori) is a common gastric pathogen that is linked to peptic ulcers, gastric cancer and systemic disorders. There is emerging evidence of an association between infection with H. pylori and chronic kidney disease and non-alcoholic hepatic steatosis, and possible renal and hepatic involvement. This study was conducted to assess the relationship between H. pylori and kidney and liver function changes in Saudi Arabian people. Blood samples were collected from 82 participants divided into two groups, namely H. pylori-infected and non-infected. Biochemical analyses were done to determine kidney (creatinine, urea, sodium and potassium) and liver function (ALT, AST, ALP, total protein and albumin). A total of 82 participants were included in the study, of whom 42 (51.2%) tested positive for Helicobacter pylori infection, while 40 (48.8%) were non-infected and served as the control group. Liver enzyme levels (ALP (64.00), AST (21.40), ALT (21.26) and GGT (20.78)) were normal and did not differ significantly between groups (p > 0.05). Regression analysis showed a significant relationship between H. pylori infection and high creatinine (OR = 6.38, p = 0.018). Borderline correlations were found for GGT (OR = 1.179, p = 0.092), uric acid (OR = 2.661, p = 0.061), and the electrolytes and lipid profiles showed no significant variation. H. pylori infection was significantly related to elevated creatinine levels, and may be affecting renal function. Most liver and electrolyte parameters were not affected. Further studies with larger cohorts are required in order to confirm these findings. Oriental Scientific Publishing Company 2026. -
An Algorithm for Cybersecurity Threats Detection in the Internet of Things using Deep Learning Approach
We perform research to develop a combined deep learning algorithm that enhances security threat detection within the Internet of Things networks. The resource variations across IoT devices create obstacles for Traditional Intrusion Detection Systems (IDSs) regarding their scalability and adaptability elements. This study explores the application of Bidirectional Recurrent Neural Networks and Long Short-Term Memory networks, which are trained on Traffic data records from NSL-KDD, a widely recognized benchmark dataset. It's a secondary dataset. This dataset is preprocessed and features are engineered to be optimized for sequential pattern recognition and handling of long-term dependency. Experimental results validate the achievement of a cross-validation accuracy of 93.40%, F1 is 91.62% and precision is 90.42%, which is greater than the individual models, such as CNN, BiRNN, or LSTM. The stacking Models Bi-RNN sequential learning and LSTM dependency retention makes the system perform better at threat classification along with elevated detection accuracy for IoT-related security issues like DoS, Probe, R2L, and U2R. The consistent performance of the model through this validation split provides evidence that the system can effectively handle IoT cybersecurity threats. 2025 IEEE. -
INTERFACING PRIMAL RELIGION OF THE HAMAI (ZELIANGRONG), CHRISTIANITY, HERAKA, AND TINGKAO RAGWANG CHAPRIAK
This chapter explores the intertwinement between four religious traditions, namely (1) Characheng (primal religion of the Hamai) and its offshoots, (2) Heraka, (3) Tingkao Ragwang Chapriak (TRC), and (4) Christianity in contemporary Hamai (Zeliangrong) communities. The influence of the primal Hamai religion on Christianity is unquestionable, and at the same time, these two traditions hold sway over Heraka and TRC in varying degrees. The impacts of the interaction are at the levels of consciousness, belief systems, practices, and values. The chapter brings out the asymmetric encounter between reformed religious traditions (Heraka) of the Hamai and the proselytisation of Christianity in the Hamai communities that had led to the extinction of the primal religion of the former. Remarkably, Heraka and TRC are counter-proselytising movements against Christianity based on the primal belief system and synthesis of Christian and Hindu belief systems. For this purpose, the research employs comparative and dialogical approaches to explore and analyse the interconnection among the above religions. It argues that the current forms of Christianity, Heraka, and TRC in Hamai tribes are unique in themselves, and at the same time, they are also cyclically inspired by one another in the process of their encounters. 2025 selection and editorial matter, Maguni Charan Behera; individual chapters, the contributors. -
Towards a Tribal Literary Criticism in India: Engaging Northeast Tribal Voices in English Literature
This article formulates and applies a tribal literary criticism to the tribal voices of Northeast India articulated in English literature. It moves beyond prevailing literary paradigms that have traditionally marginalised indigenous worldviews and viewpoints by adopting indigenous-tribal epistemologies and a decolonial approach. By applying tribal knowledge through close readings of selected tribal literary texts produced by tribal writers from the Northeastern regions of India, the study explores key concepts such as community, land, identity, and ecology, rooted in the tribal holistic worldview of the God-world-human continuum. Relying on tribal worldviews, oral traditions, memories, storytelling, and lived experiences, indigenous-tribal interpretative tools offer alternative frameworks. Furthermore, the study validates tribal ways of knowing and expands the field of literary criticism by including diverse epistemic traditions. 2026, Penerbit Universiti Kebangsaan Malaysia. All rights reserved. -
Building Digital Relationships: Social Relationship Marketing Strategies to Generative AI
Artificial intelligence (AI) is redefining business process optimization to make business efficient, cheaper, and innovative. AI streamlines operations, automates processes, and enhances decision- making to enable business to serve customers better and remain competitive. AI solutions vary from customer services, supply chain management, finance, and human resource. AI chatbots provide 24/7 customer support, and predictive analytics enable optimized sales forecasting and inventory management. AI allows savings and creation of innovations by means of customized services. Challenges that do arise include the necessity of having quality data. AI has to be trained on huge volumes of data for it to be able to make sound predictions, and poor data quality will bring about the destruction of decision- making. Last but not least, investing in AI technology and experienced personnel can be exorbitant. 2026, IGI Global Scientific Publishing. All rights reserved. -
Dual-mode chemosensor for the fluorescence detection of zinc and hypochlorite on a fluorescein backbone and its cell-imaging applications
Fluorescein coupled with 3-(aminomethyl)-4,6-dimethylpyridin-2(1H)-one (FAD) was synthesized for the selective recognition of Zn2+ over other interfering metal ions in acetonitrile/aqueous buffer (1 : 1). Interestingly, there was a significant fluorescence enhancement of FAD in association with Zn2+ at 426 nm by strong chelation-induced fluorescence enhancement (CHEF) without interrupting the cyclic spirolactam ring. A binding stoichiometric ratio of 1 : 2 for the ligand FAD with metal Zn2+ was proven by a Jobs plot. However, the cyclic spirolactam ring was opened by hypochlorite (OCl?) as well as oxidative cleavage of the imine bond, which resulted in the emission enhancement of the wavelength at 520 nm. The binding constant and detection limit of FAD towards Zn2+ were determined to be 1 104 M?1 and 1.79 ?M, respectively, and the detection limit for OCl? was determined as 2.24 ?M. We introduced here a dual-mode chemosensor FAD having both the reactive functionalities for the simultaneous detection of Zn2+ and OCl? by employing a metal coordination (Zn2+) and analytes (OCl?) induced chemodosimetric approach, respectively. Furthermore, for the practical application, we studied the fluorescence imaging inside HeLa cells by using FAD, which demonstrated it can be very useful as a selective and sensitive fluorescent probe for zinc. 2022 The Royal Society of Chemistry. -
Colorimetric and theoretical investigation of coumarin based chemosensor for selective detection of fluoride
Coumarin based Sensor 1 has been designed and synthesized to recognize fluoride ion visually with high selectivity and sensitivity over other anionic analytes through color change from very faint yellow to pink in acetonitrile. The probable binding phenomenon in solution phase has been explained by 1H NMR study of sensor 1 with different concentration of fluoride ions. The binding constant of the sensor 1 with fluoride has been determined as 3.9 104 M?1 and the lower detection limit 6.5 M of the sensor 1 towards fluoride, which has made the sensor 1 as a promising backbone for selective detection of fluoride. For the practical application, test strips based on sensor 1 were fabricated, which could act as a convenient and efficient naked eye F?test kits. The experimentally observed absorption maxima along with its binding nature with fluoride ions also have been supported through theoretical calculations using density functional theory (DFT) calculations. 2022 -
Sustainable Technologies for Recycling Process of Batteries in Electric Vehicles
The effective management of batteries has always been a key concern for people because of the imposing challenges posed by battery waste on the environment. This paper explores strategic perspectives on the sustainable management of batteries incorporating modern techniques and scientific methodologies giving batteries a second-life application. A paradigm shift towards the legitimate use of the batteries by the introduction of round economy for battery materials and simultaneously checking the biological impression of this fundamental innovation area. 2023 IEEE. -
Waves, Velocity Addition and Doppler Effect in Light of EPRs Completeness Condition
It is a standard practice to derive velocity addition rules for point particles from Galilean and Lorentz transformations in point (classical) mechanics, and to apply such rules to wave velocities for explaining Doppler effect. However, in such standard practice, it is never shown whether the equation for wave propagation actually transforms in a way such that the velocity addition rules get manifested through the equation itself. We address this gap in the literature as follows. We claim that the velocity addition for waves, being the one and only mean to explain the empirically verified Doppler effect, should be considered as an element of physical reality in accord with EPRs completeness condition of a physical theory. Therefore, the equation for wave propagation should manifest such velocity addition so as to be considered as a part of the respective physical theory of waves. We show that such manifestation is possible if and only if wave propagation is modeled with first order partial differential equations. From a historical point of view, this work settles the Doppler-Petzval debate which originated from Petzvals demand for an explanation of Doppler effect in terms of differential equations. From the foundational perspective, this work sets the stage for a renewed focus on the mathematical modeling of wave phenomena, especially in the context of various Doppler effects. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
A Hybrid Approach for Predictive Maintenance Monitoring of Aircraft Engines
The realm of aircraft maintenance involves predictive maintenance, which utilizes historical data and machine parts' performance to anticipate the need for maintenance activities. The primary focus of this paper is to delve into the application of predictive maintenance of aircraft gas turbine engines. Our methodology involves assigning a randomly chosen deterioration value and monitoring the change in flow and efficiency over time. By carefully analyzing these factors, we can deduce whether the engines are at fault and whether their condition will deteriorate further. The ultimate objective is to identify potential engine malfunctions early to prevent future accidents. Recent years have witnessed the emergence of multiple machine learning and deep learning algorithms to predict the Remaining Useful Life (RUL) of engines. The precision and accuracy of these algorithms in assessing the performance of aircraft engines are pretty promising. We have incorporated a hybrid model on various time series cycles to enhance their efficacy further. Employing data collected from 21 sensors, we can predict the remaining useful life of the turbine engines with greater precision and accuracy. 2024 IEEE. -
A novel AI model for the extraction and prediction of Alzheimer disease from electronic health record
Dark data is an emerging concept, with its existence, identification, and utilization being key areas of research. This study examines various aspects and impacts of dark data in the healthcare domain and designs a model to extract essential clinical parameters for Alzheimer's from electronic health records (EHR). The novelty of dark data lies in its significant impact across sectors. In healthcare, even the smallest data points are crucial for diagnosis, prediction, and treatment. Thus, identifying and extracting dark data from medical data corpora enhances decision-making. In this research, a natural language processing (NLP) model is employed to extract clinical information related to Alzheimer's disease, and a machine learning algorithm is used for prediction. Named entity recognition (NER) with SpaCy is utilized to extract clinical departments from doctors' descriptions stored in EHRs. This NER model is trained on custom data containing processed EHR text and associated entity annotations. The extracted clinical departments can then be used for future Alzheimer's diagnosis via support vector machine (SVM) algorithms. Results show improved accuracy with the use of extracted dark data, highlighting its importance in predicting Alzheimer's disease. This research also explores the presence of dark data in various domains and proposes a dark data extraction model for the clinical domain using NLP. 2025 Institute of Advanced Engineering and Science. All rights reserved.