bluuebunny/crossref_metadata_2025 huggingface dataset:
Humanisation of the Teacher Education Curriculum and Pedagogy in South Africa: A Literature Review
Authors: Osiesi Mensah Prince
Published: January 2025

A humanising curriculum cum pedagogy is an evolving terminology in the South African education space. Post-apartheid South Africa continues to struggle with her reasonably new democracy, which is still characterised by social injustice, inequity, racism, xenophobia, crime, and unemployment. A panacea for these is through a fully humanised education system. Underpinned by the Freirean humanising pedagogy theory, this review explored the state of a humanising curriculum and pedagogy in South Africa.Five search terms, "humanising curriculum", "humanising pedagogy", "humanising teacher education", "humanising andragogy", and “South Africa”, were used for the review. Journal articles, scholarly books, doctoral theses, and conference proceedings published in English and indexed in SCOPUS, EBSCO, PsyINFO, and Google Scholar formed the inclusion criteria. 29 published articles were initially identified, with only 7 articles meeting the set criteria. The review's findings and strategies for promoting a humanising teacher education curriculum and pedagogy are discussed. I surmise that a humanising curriculum and pedagogy is not fully operational in South African schools. I suggest that practical deliberative actions and efforts at enhancing its operations and manifestations be intensified to avoid making it rhetorical. It is anticipated that this paper will lead to a better understanding of and promote a humanising philosophy in teacher education that is geared towards changing the current situation in schools and equip curriculum designers and scholars, teacher educators, as well as pre-service and in-service teachers, to commit to a humanising curriculum and pedagogy in these current times. Thus, promoting a more humane philosophy and approach in the South African teacher education curriculum and pedagogy could be the first step in this transformation.
Institutional Barriers to Mobility and the University: Policy Issues
Authors: Vasilopoulos Andreas , Pirgiotaki Konstantina
Published: January 2025

This paper addresses the gap between policy objectives for participation in mobility and their implementation in higher education institutions. It examines potential institutional impediments to mobility at the university level. To this end, it adopts a qualitative methodological approach based on semi-structured interviews with administrators involved in implementing the Erasmus programme from a diverse range of Greek universities (purposive sampling). Our findings align with the international literature, which identifies funding, complex bureaucratic procedures, difficulties in recognising study periods, and incomplete or inadequate information about the programme as crucial factors of institutional barriers to mobility. Furthermore, the interviewees' status and geographical origin provided insights into the non-supportive institutional context of participation in mobility. The university does not address student participation inequalities, and the existing institutional framework may discourage students from participating in mobility. Sometimes, this leads to cancelling students' participation in the Erasmus programme, which challenges higher education institutions. Research findings may have practical implications for institutional policies regarding institutional barriers to mobility.
Persuasive Communication: A Vehicle for Positive Change In Medication Misuse By University Undergraduates — An Exploratory Study
Authors: Ogbodu Olubunmi
Published: January 2025

Stress levels escalating to significant proportions have been reported in university students, especially during tests and examination periods, hence the misuse of prescription and over-the-counter medications. The situation of medication misuse amongst university undergraduates in Nigeria is no exception, as found globally. Medication misuse is influenced by risk and promotive factors. Persuasive communication is a message intended to reinforce or change the responses of others and has been found relevant in behavioural health changes. It could be adopted to change medication misuse by university students. The study was done at the University of Lagos in Lagos State, Nigeria. An exploratory, descriptive research design using one-on-one in-depth interviews to collect data from 20 respondents but with data saturation after the 12th participant. The respondents were selected using a purposive sampling method with defined inclusion and exclusion criteria for registered university undergraduates and academic and non-academic staff of the university. Data analysis was performed using Tesch’s Open Coding method. The study revealed the risk and promotive factors of medication misuse by university students during examination periods, family income, gender, peer influence, and ease of access to medications. The study advocates for the adoption of persuasive communication by the university management to communicate frequently the risks of medication misuse to positively impact students in taking appropriate initiative for their health. Persuasive communication to mitigate medication misuse by university students, including the positive use of the media and celebrities and increased sensitisation by the university management on the dangers of medication misuse, is discussed and recommended. This study contributes to effective public health information dissemination, discouraging medication misuse among university students. The limitations and recommendations of the study were discussed.
Archaeopteryx Fossil: A Paleontological Puzzle Activity to Engage Undergraduate Science Majors
Authors: Unger Shem , Rollins Mark
Published: January 2025

Developing strategies for teaching undergraduate science majors about evolution and the earth's history presents student comprehension and engagement challenges. Pedagogical methods outside the typical Socratic lecture method are needed to allow students to develop a scientific and collaborative mindset that fosters taxonomic and geological concepts fundamental to understanding evolution. We created an inquiry activity to encourage group learning alongside promoting discovery and thinking like a scientist, which involved sequentially revealing only portions of an Archaeopteryx fossil in sections. The mystery taxa or puzzle activity was given to first-year college students. We quantified the activity using a combination of a worksheet for taxa and a post-activity survey gauging student perceptions of this activity using descriptive statistics and a Chi-Squared analysis. Student feedback on the activity as engaging was overwhelmingly positive (98.6%), with the majority of students reporting their increased understanding of the process of science for identification of fossils X2(1,n = 140) = 59.961, p < 0.001, and as a fun, informative method for learning about fossils, X2(1,n = 140) = 60.682, p < 0.001. By the end of the activity, most students could identify the Archaeopteryx fossil as a bird based on responses, particularly when the complete fossil was uncovered. This fossil puzzle activity can easily be modified to be used at any academic level (e.g., K-12, pre-K, university) to engage future scientists using alternative fossil specimens with specific morphological traits or incorporated into learning modules focusing on evolution, zoology, anatomy, or paleontology.
Effects of Leadership Styles on Job Satisfaction of Basic School Teachers at the Sagnarigu Municipality in the Northern Region of Ghana
Authors: Bariham Iddrisu , Sarah Mahamudu
Published: January 2025

The leadership styles adopted by school administrators are one of many variables that impact teachers' productivity and work satisfaction in educational settings. Schools with authoritarian leaders demotivate teachers and create unhappy employees, whereas schools with democratic and transformative leaders encourage creativity and innovation and increase employee satisfaction. Therefore, this study looked at how leadership style affected the job satisfaction of elementary school teachers in Sagnarigu Municipality. Four assumptions served as the foundation for the cross-sectional design used in the study. 68 headteachers and 242 teachers were sampled to complete a five-item Likert scale questionnaire. Using Cronbach's alpha formula, the questionnaire items' internal consistency (reliability coefficient) was verified. Victor's leadership styles scale, which had five items, had an alpha of r=0.741 while the Teachers' Job Satisfaction Scale (TJSS) had an alpha of r=0.670. The study found that a democratic leadership style was the most frequently employed in the management of the schools. It was further revealed that teachers were generally dissatisfied with their jobs. The study also discovered that the democratic, authoritarian, and laissez-faire leadership styles have no statistically significant effect on teachers' job satisfaction. However, the transformational leadership style correlated with the job satisfaction of the teachers. The study recommends capacity building for basic school heads, a blending of the leadership styles in managing the schools, increasing salaries for teachers to boost their morale for high productivity, and further research to unravel the reasons why teachers were dissatisfied with their jobs.
Career Trajectories of Bachelor of Science in Industrial Education Graduates: A Post-Graduation Tracer Study
Authors: Montuerto Ronilo Capisnon , Arevalo Mary Florebelle Noynay
Published: January 2025

Tracking the whereabouts of the graduates provides empirical data on the employment profile of the graduates. This study was conducted to gather data on the employment of the graduates of Bachelor of Science in Industrial Education program of Palompon Institute of Technology Tabango currently renamed to Bachelor of Technical-Vocational Teacher Education. 58 out 74 (or 78 %) graduates of Bachelor of Technical-Vocational Teacher Education from year 2016-2019 participated in this study. The instrument used by the researchers in the data gathering was the Graduate Tracer Study developed by the Commission on Higher Education (CHED).
Findings make known that 76% (or 44 out of 58) of the respondents are employed. Moreover, 61% (or 27 out of 44) perceived that their current job is related to the program that respondents took up in college. Furthermore, 55% (or 24 out of 44), 9% (or 4 out of 44), 34% (or 15 out of 44), 2% (or 1 out of 44) has a regular, temporary, contractual, self-employed employability status, respectively. When asked the reasons why some graduates are not yet employed, top 1 reason is they did not look for a job, followed by no job opportunity, lack of work experience and family concern, then, advance or further study, and last of all is the health reason. Also, when asked what skills learned in college did the graduates find very useful in their job, top 1 is human relation skills, followed by communication skills, information technology skills, critical thinking skills, problem-solving skills, and lastly, entrepreneurial skills. To further improve the employability rate of the BTVTED program, among the researchers’ recommendations is that the administration should tap linkages to other agency or companies that can provide employment opportunities to the graduates and may conduct a job fair in partnership with the LGUs, NGOs, and other governing bodies. The administration should also establish linkages in the industry and create a student immersion program to develop the human relation skills, communication skills, IT skills, critical thinking skills, problem solving and entrepreneurial skills. Revisit of the curriculum is also recommended to ensure that the courses offered are relevant to the nature of their job in the future and in order to find some areas that needs improvement.
Maintaining the Heritage Language in the Family’s Linguistic Cocktail
Authors: Zarei Nafiseh , Tagadiad Michael John
Published: January 2025

This study mainly explored the language planning vis-à-vis heritage language maintenance of a migrant multilingual Igorot family in Malaybalay, Mindanao. By employing descriptive qualitative research design via Focus Group Discussion (FGD), this study revealed that the family’s language planning is covert, with the father acting as an unconscious language planner. This role has helped maintain the heritage language, ‘Kankanaey,’ within a multilingual linguistic cocktail, which includes Ilocano, Filipino, English, and Bisaya. Though multilingualism gives birth to this linguistic cocktail in the family to communicate in different communication situations (i.e., family, family-relative, social, and educational affairs), it does not directly threaten the maintenance of their heritage language because it is practiced between and among family members and relatives. The family’s use of Kankanaey within their inner circle, despite the grandson’s unfamiliarity with it, does not suggest a gradual loss of the language. As the grandson is still in his teenage years, there is potential for him to acquire and maintain the heritage language. This study further demonstrated that the language planning of the family serves as a concrete basis for the local legislators to make policies concerning education, specifically a policy involving the recognition of heritage languages that form students’ linguistic identities, a policy capable of securing these languages to serve as bridging languages to learn best a second language.
Empowering Literacy: A Comprehensive Study of Alternative Learning System (ALS) Implementation in the 4th Congressional District of Iloilo, Philippines
Authors: Libo-on Joefel Toledo , Catunao John D
Published: January 2025

The study investigates the status of the Alternative Learning System (ALS) implementation in the 4th Congressional District of the Iloilo province, Philippines, emphasizing the significance of literacy in individual well-being and societal contribution. The research addresses the persistent issue of illiteracy, particularly in regions such as South Asia and sub-Saharan Africa, impacting economic growth. Focusing on the Philippines, a country grappling with high poverty rates, the study underscores the plight of Out-of-School children, youth, and adults, linking their challenges to educational opportunities. The ALS program, implemented by the Philippine government, emerges as a vital strategy to address illiteracy and educational gaps. A ladderized, modular non-formal education program, ALS caters to diverse learners, including dropouts, working individuals, and senior citizens. The subsequent sections present a comprehensive descriptive- correlational analysis of ALS implementation in the 4th Congressional District, employing a mixed methodology. Results indicate a commendable “very good” status for ALS basic literacy and an “excellent” status for the accreditation and equivalency program. Challenges, though present, are labeled as “less serious,” with variations across municipalities. The lack of significant differences in implementation status and challenges among municipalities and classifications suggests a uniform and effective ALS implementation. The study contributes valuable insights for targeted interventions to further enhance ALS efficacy. Future research avenues are proposed to delve deeper into identified challenges, offering tailored solutions for continuous improvement in basic literacy and accreditation and equivalency programs. Overall, the study provides a foundational understanding of ALS implementation in the district, emphasizing its positive trajectory and resilience in the face of challenges.
Outcomes of the Technical Skills Training Completed by Out-of-School Youths and Adults
Authors: Ontolan Chaseur Leo Vann O. , Tado Anna Wilda C. , Secretaria Kenneth C. , Abatayo Junifer
Published: January 2025

This study evaluated the outcomes of the Alternative Learning System-Education and Skills Training (ALS-EST) project implemented at Bukidnon State University, focusing on technical skills training for out-of-school youths and adults (OSYAs) in healthcare services. Utilizing GrantCraft's Theory of Change and the Outcomes Harvesting methodology, the research aims to assess the direct and indirect impacts of the program on participants' employability and socioeconomic status. The ALS-EST project, in collaboration with the Department of Education and other local partners, offered comprehensive training in healthcare to enhance participants' skills to help them socio-economically. Findings indicate significant improvements in the participant's ability to apply acquired skills in professional settings, pursue further education, and achieve economic betterment. The training not only equipped the training completers with essential job-related skills but also fostered a sense of community and enhanced their overall confidence. This study underscores the importance of alternative education programs in providing viable career pathways and improving the quality of life for marginalized populations. The study concludes that substantial gains in technical skills and employability, as well as community and professional development, with direct outcomes including workplace skill application and community service, and indirect outcomes involving knowledge sharing and a culture of continuous improvement. It is recommended to include expanding the training program to cover more healthcare industry skills and facilitating certification and job placement through collaborations with relevant authorities and local government units. These steps aim to enhance participants' employability and socio-economic status further, contributing to their long-term development.
Piecewise adaptive weighted smoothing-based multivariate rosenthal correlative target projection for lung and uterus cancer prediction with big data
Authors: V. Babydeepa  , K. Sindhu
Published: January 2025

Cancer is the uncontrolled growth and spread of abnormal cells in the body. Early detection and prediction of cancer are crucial aspects of modern healthcare aimed at greatly improving the chances of survival for patients by reducing mortality rates and the number of people affected by this disease. Due to the large volume of data generated in the medical industry, accurate cancer detection is a challenging task. Many cancer classification systems using machine learning and deep learning models have been developed but accurate cancer detection with minimal time consumption remains a major challenging issue in the big data applications. To enhance the accuracy of cancer detection with minimal time, the Piecewise Adaptive Weighted Smoothing-based Multivariate Rosenthal Correlative Target Projection (PAWS-MRCTP) technique is introduced. This technique aims to detect lung and uterus cancers while leveraging big data. The proposed PAWS-MRCTP technique comprises three main processes namely data acquisition, preprocessing, and feature selection. In the data acquisition phase, a large number of cancer patient data are collected from lung cancer and uterus cancer detection datasets. Subsequently, the collected patient data undergo preprocessing. The preprocessing stage comprises three key processes namely handling missing data, noisy data, and outlier data. Firstly, the proposed PAWS-MRCTP is employed to address missing values, utilizing the Piecewise Adaptive Constant Interpolation method based on multiple available data points. Noisy data are identified using Gower's weighted smoothing technique, which detects data containing random variations or errors. Subsequently, outlier data are identified and removed by applying Peirce's statistical test. As a result, the pre-processed dataset is obtained resulting to minimize the time complexity. With the pre-processed dataset, the feature selection process is carried out to minimize the dimensionality of the large dataset. The proposed PAWS-MRCTP technique utilizes the Multivariate Rosenthal correlative target feature projection technique to identify the most relevant features. By selecting significant features, this approach enhances the accuracy of lung cancer and uterus cancer detection with minimal time consumption. Experimental assessment is conducted with different evaluation metrics such as cancer detection accuracy, precision, and cancer detection time and space complexity. The observed result shows the effectiveness of the proposed PAWS-MRCTP technique with higher accuracy with minimum time than the existing methods.
A hybrid feature selection and generative adversarial network for lung and uterus cancer prediction with big data
Authors: V Babydeepa  , K. Sindhu
Published: January 2025

Among all diseases affecting humanity, lung cancer has consistently stood out as one of the deadliest. It ranks among the most prevalent cancers and is a significant contributor to cancer-related deaths. The disease is often asymptomatic in its early stages, making early detection extremely challenging. To enhance the accuracy of cancer detection with minimal time, an effective hybrid feature selection and classification model is developed in this research for the efficient detection of detect lung and uterus cancers while leveraging big data. The Piecewise Adaptive Weighted Smoothing-based Multivariate Rosenthal Correlative Target Projection (PAWS-MRCTP) comprises three main processes namely data acquisition, preprocessing, and feature extraction.  In the data acquisition phase, a large number of cancer patient data are collected from lung cancer and uterus cancer detection datasets. Subsequently, the collected patient data undergo preprocessing. The preprocessing stage comprises three key processes namely handling missing data, noisy data, and outlier data. Firstly, the proposed PAWS-MRCTP is employed to address missing values, utilizing the Piecewise Adaptive Constant Interpolation method based on multiple available data points. Noisy data are identified using Gower's weighted smoothing technique, which detects data containing random variations or errors. Then the Improved Particle Swarm Optimization (IPSO) with fuzzy possibility C-Means clustering (FPCM) is introduced for the data clustering. And then the hybrid feature selection is performed using the ANFIS and Modified Chicken Swarm Optimization (MCSO). Finally, the classification of uterine and lung tumors is done using the Generative Adversarial Network (GAN). Consequently, in the experiments, the proposed model beats existing classifiers in detection accuracy while consuming the least time.
Familial support of rural elderly in indian family system: A sociological analysis
Authors: Prabu Gopal  , M. Jeyaseelan
Published: January 2025

A joint family system is considered as a basic feature of our rural villages. Village people adopt this system as they employ this family living in arrangement in managing their agricultural activities because of its extended and communitarian form. Hence, the older rural people do their best approaching their close relatives and try to develop the confidence level of their own young and married family members. The family persistence is always successful. However, there are frequent instances that due to failure in the negotiation, there is always family separation, which happens in the case of both elderly father, mother, and three married sons, as a result of which, elderly individual who was always leading a popular and family head position do not get a proper treatment, which leads to a great loss affecting badly for his age-long family support and the same result in discontinuation of the elderly leader, who do not get any respect and reputation from others. This research helps in social science research, which was conducted in the selected areas of Salem district, in which systematic random sampling was employed to choose 400 aged in the rural region. The purpose of this research is to determine the family support available to the rural aged with the Indian family system. In the rural context, the elderly individual experiences many problems as they are based on the social, economic, and cultural factors in rural areas. In this research, 400 elderly individual were chosen and this study is conducted using descriptive design, which involves survey research method. The findings reveal that, rural aged face a lot of problems in the process of determining their family support.
Floristic composition in Paramananda Devara Gudda A sacred grove at Lingadahalli Village Devadurga Taluk Raichur District Karnataka, India
Authors: Theophilus Deenadayal  , Tarun Jain
Published: January 2025

Sacred groves are an important feature of any preservation of diversity as they are ecological, cultural and medicinal assets. A survey conducted in this study aimed to determine floral diversity in the Paramananda Devara Gudda sacred grove utilizing Shannon–Wiener (SW = 4.29), Simpson’s Diversity (SD = 1.00) and Species Diversity (SD = 0.78). The analysis pointed out a number of genera, that is, Crotalaria and Indigofera having the most species (10 species of each) other than Blumea and Vachellia with 5 species each respectively. Other genera, which also pointed to the high diversities within the site included Cleome, Cyperus, Phyllanthus and Ziziphus. This grove is not only valuable in terms of having unique and outstanding features as ecological and cultural taxa, nitrogen-fixing plants including Indigofera and Crotalaria, Phyllanthus, Ocimum as medicinal plants, and multipurpose plants including Ziziphus. High indices are explained by the values of effective species equality and the species sharing minimal impacts, the low-representation genera match with called for conservation program. The Paramananda Devara Gudda is a holy grove performing vital ecosystem services of conservation and hence is a very important biore-cultural site. The present research focuses on the community approach to sustainable management of regional diversity and cultural resources.
Comparison of the acute metabolic and cardiovascular effects of electrical stimulation and voluntary exercise
Authors: Goyal Divya , Chahal Aksh , Bhatnagar Aashi , Vishakha  , Malhan Sheetal , Trivedi Vishwajeet
Published: January 2025

Despite being marketed as a fat reduction tool, the effects of electrical stimulation on blood lipid profiles remain underexplored. This study aimed to compare the acute effects of electrical stimulation and voluntary exercise on heart rate, respiratory rate, blood pressure, and blood lipid parameters, including cholesterol, triglycerides, and HDL levels. Ten healthy males (mean age: 24.6 ± 1.35 years) participated in a randomized crossover study. Each underwent 20 minutes of either electrical stimulation (faradic current at 50 Hz, 3 seconds stimulation, 6 seconds rest, applied bilaterally to quadriceps) or cycling (60-70% of maximal heart rate), with a 7-day washout period between sessions. Pre- and post-intervention measures included heart rate, respiratory rate, blood pressure, cholesterol, triglycerides, and HDL levels. Electrical stimulation significantly altered post-treatment values for cholesterol (183.6 ± 10.94 vs. 185.5 ± 9.70 mg/dL), triglycerides (127.62 ± 29.52 vs. 128.5 ± 29.07 mg/dL), HDL (40.30 ± 4.69 vs. 39.17 ± 5.23 mg/dL), and heart rate (65.60 ± 7.79 vs. 61.40 ± 8.67 beats/min) compared to pre-treatment values (p < 0.05). Similar changes were observed in the cycling group. These findings suggest that electrical stimulation has the potential to influence blood lipid profiles, comparable to moderate-intensity cycling, highlighting its potential as an alternative intervention and warranting further research.
Analysis of distributions using stochastic models with fuzzy random variables
Authors: Senthil Murugan C  , Dhanabal Vijayabalan , Sukumaran D  , Suresh G  , Senthilkumar P
Published: January 2025

The significance of this paper lies in the investigation of a novel method for comparing the expectations of stochastic models in fuzzy settings. In order to comprehend actuarial science and economical modeling, stochastic models are necessary. The primary benefit of the paper is to comprehend the novel ideas of stochastic comparison of stochastic models built on the exponential order. We applied the fuzzy mean inactivity time order definition, solved the preservation properties and theorem, and created a new definition. Applications involving stochastic models are presented.
Existence and uniqueness of solutions for exponential fractional differential equations
Authors: Kamble Rajratna M.  , Kulkarni Pramod R.
Published: January 2025

In this paper, we  have discuss some results on existence and uniqueness theorems for exponential definition fractional differential equation of orderAnd proved  theorem existence and uniqueness theorems for sequential exponential definition fractional differential equations of order n  and give examples to support results.
Polyphenolic compounds as novel reno-modulatory agents in the management of diabetic nephropathy in Wistar rats
Authors: Chhetri Royan , Kumar N Prem
Published: January 2025

Diabetic nephropathy (DN), a prevalent diabetes-related complication that leads to end-stage kidney failure due to functional changes in the renal system. Contemporary antidiabetic interventions effectively fail to prevent or slow down the course of diabetic kidney damage. The current study investigates the impact of Coccinia indica ethanolic leaf extract (CILE) both individually and combined with hesperidin, a bioflavonoid, to mitigate the advancing renal damage induced by non-insulin-dependent diabetes mellitus in a rodent model. DN was triggered in experimental rats by administering a high-fat diet and, thereafter, by a single injection of streptozotocin (35 mg/kg, intraperitoneally). The experimental groups received CILE, hesperidin (high and low dose), C. indica in conjugation with low-dose hesperidin and metformin alone during the treatment period. The study evaluated several parameters, including serum glucose, antioxidant levels, blood urea nitrogen, urine protein levels, creatinine, and inflammatory mediators (interleukin-6 and tumor necrosis factor-alpha). These parameters were also studied in negative control animals to understand and compare the therapeutic potential of polyphenolic compounds. The histomorphological study coincides with the elevated biomarkers observed in the non-intervention groups. The treatment with different predetermined therapies normalized all biomarkers toward normal levels, including histopathological changes. Therefore, administration of hesperidin and CILE showed significant benefits in delaying the progression of kidney damage and presents promising potential therapy for managing DN.
Smart flood monitoring in Guwahati city: A LoRa-based AIoT and edge computing sensor framework
Authors: Mandal Rupesh , Sharma Bobby , Chutia Dibyajyoti
Published: January 2025

In today’s context, urban flooding has emerged as a pervasive and significant global challenge, resulting in substantial economic losses spanning both human lives and property damage. With a concerning rise in urban flood-related fatalities and financial impacts, there’s an urgent call for enhanced flood risk management strategies. Although floods, as natural disasters, cannot be entirely prevented or eliminated, their catastrophic effects can be significantly reduced or mitigated. Cutting-edge technologies like the internet of things (IoT) and artificial Intelligence offer promising solutions for flood prediction. These advancements facilitate early warning systems, enabling pre-emptive evacuation measures to safeguard lives and minimize economic repercussions. This work aims to develop and implement a system leveraging IoT-derived data with an edge computing framework. It also uses machine learning techniques for fuzzy-based fused framework to provide rainfall prediction early flood warnings, focusing on risk mitigation as a proactive approach to address this pressing issue.
Optimizing power converters for enhanced electric vehicle propulsion: A novel research methodology
Authors: P Ravi Kumar , C. Gowri Shankar
Published: January 2025

This research paper presents a novel methodology for enhancing power converters in electric vehicle (EV) propulsion systems, focusing on optimizing efficiency, reliability, and performance. It integrates theoretical analysis, simulations, and practical experimentation to address current challenges in power converter technology for EVs. The study begins with a literature review to identify gaps and emerging trends in power converter technologies. A theoretical model is then proposed, incorporating advanced semiconductor materials, innovative circuit topologies, and improved thermal management to boost efficiency and power density. Simulation tools, such as finite element analysis and system-level modeling, are used to validate the model and optimize design parameters. These simulations predict converter behavior under various conditions and loads, providing insights for performance improvements. A prototype power converter based on the optimized design is developed to validate the theoretical predictions. Experimental data is collected through rigorous testing, evaluating factors like efficiency, thermal performance, and response time. The experimental results are compared with simulation outcomes to verify the accuracy of the methodology. The study also explores bidirectional power flow for vehicle-to-grid (V2G) applications, assessing the impact on power converters and their role in energy exchange between EVs and the grid. This research offers a systematic approach to advancing power converters in EV propulsion systems, combining theoretical analysis, simulation-based optimization, and practical testing to contribute to the development of sustainable, high-performance electric transportation.
A lightweight selective stacking framework for IoT crop recommendation
Authors: S. Sindhu  , L. Arockiam
Published: January 2025

The integration of the internet of things (IoT) into precision farming has revolutionized agricultural practices by enhancing resource management and crop productivity through real-time data monitoring and automation. Precision farming leverages IoT technologies to monitor critical environmental factors such as soil moisture, temperature, humidity, and nutrient levels, enabling farmers to make data-driven decisions for optimizing crop growth. Despite these advancements, improving the accuracy and efficiency of IoT-based crop recommendation systems remains a key challenge, particularly in resource-constrained environments. This study aims to enhance the predictive performance of IoT-based crop recommendation systems by developing a novel stacked deep ensemble learning model. The proposed model, termed lightweight selective stacking with deep ensemble learning (LSSDEL), focuses on reducing computational complexity while maintaining high predictive accuracy. Key methods employed include selective model stacking, L1 regularization for model pruning, gradient-free model aggregation, and the implementation of an early stopping mechanism. The system is validated using real-world IoT agricultural datasets, emphasizing its scalability and practical applicability. Findings from the study demonstrate that the LSSDEL model outperforms traditional models, achieving a prediction accuracy of 97.80% and significant improvements in precision, recall, and F1-score. Furthermore, the proposed model reduces execution time by 16.7% compared to existing approaches, confirming its computational efficiency.
Machine learning-based ERA model for detecting Sybil attacks on mobile ad hoc networks
Authors: R. Kalaiselvi  , P. Meenakshi Sundaram
Published: January 2025

Mobile ad hoc networks provide a substantial security threat because they lack central management and sufficient resources. These networks function autonomously without any central authority regulating the inclusion or removal of nodes. Nodes have the autonomy to choose when to join or quit. Dynamic multi-hop networks, either stationary or mobile, provide quick and simple access to data. Predicting the evolution of MANET can be challenging due to the network’s dispersion and self-organization, as well as its unpredictable and constantly changing topology. The independent organization of nodes in MANETs, coupled with their dispersion, may complicate the prediction of the network’s future growth due to its unstable and constantly changing structure. A Sybil attack, a deceptive tactic, involves a small number of individuals creating multiple counterfeit identities to gain dominance over a substantial portion of the system. To deceive legitimate users into believing that their system is utilizing their identities, the malicious attacker node adopts numerous identities. An attacker aims to gather a substantial number of node IDs, potentially generated at random, to appear and function as distinct nodes. Within the peer-to-peer overlay, the enemy can approach a single object or a group of objects by adopting different identities. Mobile ad hoc networks are intrinsically less secure than wired networks due to inherent security vulnerabilities and limited energy resources. To enhance detection accuracy, it is recommended to employ an ensemble regression arboretum model, which is a type of machine learning prediction model. This study proposes a machine learning-based method for detecting Sybil attacks in MANETs by collecting network metrics such as traffic characteristics, communication patterns, and node behaviors.
A novel approach to heart disease classification using echocardiogram videos with transfer learning architecture and MVCNN integration
Authors: Deepika S  , Jaisankar N
Published: January 2025

The echocardiogram, also known as a cardiac ultrasound, captures real-time images of the heart’s chambers and valves. Ultrasonic waves are used in this method to penetrate the skin and generate the pattern of the heart’s movement, allowing healthcare professionals to assess its overall function. In this research study, we propose a novel approach for classifying heart diseases relying on echocardiogram videos using transfer learning and ensemble methods. The approach involves using pre-trained convolutional neural network models such as VGG19, Densenet201, and Inceptionv3 as feature extractors and then training a classifier on top of these extracted features. The pre-trained models have been trained on large datasets with millions of images, making them highly effective feature extractors for various computer vision tasks. The main objective is to leverage the learned representations from these models and apply them to echocardiogram videos for accurate classification of heart diseases. The novel integration of MVCNN (pre-trained convolutional neural network models VGG19, Densenet201, and Inceptionv3) with ensemble methods has led to a significant increase in accuracy, achieving an overall accuracy of 98.09% in classifying heart diseases using echocardiogram videos and achieved AUC-0.82% After implementing the novel integration.
A blockchain-integrated smart healthcare framework utilizing dynamic hunting leadership algorithm with deep learning-based disease detection and classification model
Authors: Lakshminarayani A  , A Shaik Abdul Khadir
Published: January 2025

The healthcare sector has embraced a digital revolution driven by modern technology. Smart healthcare solutions improve patient care by addressing the challenges of traditional methods using large-scale sensor devices. Blockchain (BC) technology ensures secure, decentralized storage and sharing of medical data, fostering intelligent healthcare ecosystems. Robotics and machine learning (ML) also benefit from shared medical data. This manuscript introduces a blockchain-integrated smart healthcare framework utilizing a dynamic hunting leadership algorithm for deep learning-based disease detection and classification (BSHDHL-DLDDC). It focuses on accurate disease diagnosis using deep learning on medical images. BC technology enables secure, tamper-proof storage and privacy-compliant data sharing. Adaptive bilateral filtering (ABF) reduces noise while preserving key image details. An enhanced CapsNet model captures spatial relationships for improved feature extraction. A bi-directional gated recurrent unit (BiGRU) classifier detects and classifies diseases, with performance refined via a dynamic hunting leadership (DHL) algorithm. Simulations confirm the framework’s effectiveness, demonstrating better results compared to existing methods.
Early detection of fire and smoke using motion estimation algorithms utilizing machine learning
Authors: Suprabha Amit Kshatriya  , Arvind R Yadav
Published: January 2025

An essential part of early warning and fire incident prevention in video surveillance systems is fire detection. The present study presents methodology that integrates motion estimation methods with the state-of-the-art convolutional neural network (CNN) architecture, YOLOv5, to provide effective fire detection. The methodology combines motion estimation techniques to improve the detection of dynamic changes suggestive of fire in video frames by the YOLOv5 model. The model incorporates motion analysis techniques, such as optical flow, to capture the spatial context and temporal relationships that are essential for differentiating between fire incidents and background activities. The research makes use of annotated datasets that cover a range of fire scenarios as well as non-fire activities, which guarantees reliable training and assessment of the YOLOv5 model. The outcomes of the experiments show how well the suggested strategy works to achieve high detection accuracy and real-time processing capabilities. Comprehensive performance indicators and comparison analysis are used to confirm the model’s ability to accurately pinpoint flames in the presence of changing ambient variables and motion dynamics. By utilizing YOLOv5 and motion estimation algorithms, this research advances the field of fire detection technologies and provides a scalable and effective solution that can be integrated into emergency response frameworks, smart cities, and surveillance systems. The results highlight the possibility for improved situational awareness and proactive fire management through the integration of CNN architectures with motion analysis techniques. This abstract highlights the improvements in accuracy and real-time applicability of YOLOv5 with motion estimation methods for fire detection, outlining the research emphasis, methodology, experimental validation, and possible consequences.
Digital transformation in management education: Bridging theory and practice
Authors: Sandip Sane  , Diksha Tripathi  , Nitin Ranjan
Published: January 2025

The way theoretical knowledge is taught and utilized in practice has been completely transformed by the digital transformation of management education. This research investigates the ways in which digital tools, platforms, and technology have impacted teaching approaches and the learning environment in management education. The convergence of digital platforms is reducing the gap between theory and practice, providing students with chances for practical learning. With an emphasis on how well digital transformation aligns theoretical frameworks with real-world implementations, this study attempts to evaluate how it contributes to closing this gap. To learn more about the opinions of educators, students, and educational administrators on digital tools in management education, a survey with 300 participants was carried out. The report demonstrates how technologically enhanced learning platforms are transforming curriculum design, skill development, and course delivery in management programs. The results highlight how crucial it is to adapt instructional strategies in order to meet the needs of the contemporary corporate environment.
An assessment of growth indicators and intricacies of Udyam entities in the post-pandemic era
Authors: Vishnu Prasad C  , Ramaprabha D
Published: January 2025

Udyam registered entities constitute the predominant share of budding business units in India. Udyam registration has in a sense, revolutionized the formalization of the small business sector in India by way of undertaking unprecedented measures and efforts to bring informal units to the ambit of incorporation, especially at a time when these units were trying to insulate themselves against economic shocks caused by the Covid-19 pandemic. Three years down the line, the study aims to have a look at service enterprises, consisting of both trading and non-trading units, to measure their business growth and performance with reference to certain indicators, viz., sales, profits, assets, capital and customer base. An attempt is made to regress profits with the other key indicators. The study also makes a note of the directional relationship that is possessed by each of these indicators with the other parameters. It is observed that building wealth in the form of better assets should be the prime focus of small units, as they possess very good statistical association with returns. Also, sensitizing MSMEs on the role of Udyam, especially in terms of solving and easing out challenges associated with financing, can be instrumental.
The influence of attitude on green-cosmetics purchase intention (pi) in central Kerala
Authors: Sabana Backer  , Prasanth A.P
Published: January 2025

This study, focus on the relationship between consumer perceptions of eco-friendly cosmetics and how that might influence their purchase intention. A systematic questionnaire was utilized to collect data from 387 consumers of green cosmetics from the districts of Ernakulam, Thrissur, and Alappuzha. The study’s hypotheses were assessed using a Structural Equation Model (SEM). The findings of this study demonstrate a connection between consumers’ purchasing intentions (PI) and their attitudes toward environmental knowledge (EK), environmental concerns, subjective norms, online accessibility, and greenwashing. This study offers empirical proof that businesses can increase sales by influencing consumer perceptions.
A survey of attitude and behavior of Indian equity investors towards cryptocurrencies: Using smart-PLS and systematic equation modeling (SEM) approach
Authors: Madhu Bala Sharma  , Yadav Pooja
Published: January 2025

There hasn't been much study done specifically addressing the attitudes and behaviours of the Indian equities market investors towards cryptocurrencies. The main goal of this investigation is to explore the attitude and behaviour of the retail investors of equity market towards cryptocurrencies with context to India. The study included 200 retail investors of the Indian equity market with snowball sampling method. Smart PLS and SPSS were applied to check the research hypothesis. The outcome revealed that investors are aware but majority of the investor respondents still have no investment experience in cryptocurrency. Further, the research showed the impact of perceived ease of use (EU) and perceived benefits (PB) on both attitude as well as on behavioural intention towards cryptocurrency investments. Vulnerability didn’t have no significant impact on attitudes but did affect behavioural intentions, indicating the importance of addressing perceived risks to foster cryptocurrency investment. To enhance the cryptocurrency adoption, platforms required to prioritize the ease of use, clear communication of benefits and strategies to mitigate the investor’s concerns about risk. This study offers the new perspectives to aid financial institutions, government regulatory bodies and future researchers in comprehending the changing scenario of equity investors’ behaviour and attitudes regarding cryptocurrencies in India.