Impact of values and psychographic traits on compulsive buying behaviour in fast fashion brands
The Fast Fashion is emerging as a rapidly growing category in the Indian garment retailsector. Fast Fashion provides the latest trendy fashionable affordable clothing to consumersmultiple times in a year. The pressure of fast fashion influences the buying behavior ofconsumers resulting in compulsive purchasing tendencies (Johnson & Attmann, 2009). Theresearch study focuses on the compulsive buying behavior of Fast Fashion brand consumers.In India, Fast Fashion is emerging as an important and growing category in the garment retail. Because of rapid fashion cycles, what is ???in??? is always changing, causing consumers to feel pressure to continually update their wardrobes (Cwerner, 2001).Compulsive buying consumers buy more frequrntly without controlling their the urge to purchase(Muller, et al., 2015). The significant percentage of fast fashion buyers qulify as ???excessive shoppers??? and this international phenomenon is spreading around the world. (Greenpeace International, 2017). Shopping for clothes reflect broader values (Tatzel, 1982) and for the retailers to be successful the knowledge of consumers about culturally-defined values is important (Hyllegard et al., 2005).The extensive review of literature shows that there is a lack of research both in academic and marketing aspects regarding Compulsive buying behaviours of Fast Fashion consumer with respect to value psychographic traits. The objective of the research study is to understand the effect of Values on Compulsive Buying Behaviour of Fast Fashion Brands and to analyse the mediating vi effect of Psychographic Traits (Fast Fashion involvement, Fashion???Consciousness and Innovativeness) on Compulsive Buying Behaviour.The study used List of value (LOV) Scale to find the impact of consumer values on compulsive buying behaviour. The psychographic traits are used as intervening variable. The survey is conducted for participants aged 18-25 yrs. Analysis of data is done using Factor analysis, Anova, Multiple Regression. The study will help marketer of Fast Fashion clothing to frame their product and communication strategy in such a way that it appeals to the consumers with required psychographic traits and values.
Emoji Sentiment Analysis of User Reviews on Online Applications Using Supervised Machine Learning
Artificial neural network; K-Nearest neighbors; Logistic regression; Machine learning; Naive bayes; Natural language processing; Random forest; Sentiment analysis; Supervised
Analyzing the sentiment behind emojis can provide valuable insights into the emotional context and user sentiment associated with textual content. To conduct a comparative analysis of diverse supervised machine learning models that can achieve the highest level of accuracy in Emoji Sentiment Analysis is the purpose of this research. Five machine learning models used in this research are K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Logistic Regression, Naive Bayes, and Random Forest. The experimental process resulted in ANN and KNN models giving an accuracy of 92%. The ANN model shows its proficiency in effectively managing large datasets. ANN also supports fault tolerance. The KNN model refrains from conducting calculations during the training phase and only constructs a model when a query is executed on the dataset. This characteristic makes KNN particularly well-suited for data mining. Both ANN and K-NN excelled in the experimental study due to these distinctive attributes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 257-267.
Springer Science and Business Media Deutschland GmbH
Swetha Cordelia A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560074, India
Enhanced Artificial Neural Network for Emoji Sentiment Analysis
Artificial Neural Networks (ANNs); Computational efficiency; Emoji sentiment analysis; He and Xavier initialization; Sentiment classification; Supervised machine learning; Text mining; Weight initialization strategy
Emojis enhance textual communication by conveying emotions and providing contextual richness. This study compares the performance of supervised machine learning models such as Naive Bayes, Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), and Artificial Neural Networks (ANNs) for emoji sentiment classification. A major addition in this study is the enhancement of the ANN model using an informed weight initialization technique, which speeds up convergence and reduces training time while maintaining improved performance. The experimental results showed that the Enhanced ANN (EANN) model obtained 94% accuracy, a 2% improvement over the baseline ANN model, while lowering training time from 45 to 18 units (60% decrease), highlighting the importance of initialization strategies in deep learning. The initialization method helped the EANN network avoid overfitting, resulting in increased generalization and accuracy. Proper initialization balanced the gradients during backpropagation, avoiding gradient issues that limit deep networks. Also, the informed weight initialization guaranteed that the EANN began training closer to an optimal solution, lowering the possibility of becoming confined in suboptimal local minima. The findings from this study contribute to advances in sentiment analysis and text mining, particularly in terms of improving the efficiency and accuracy of deep learning approaches. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Swetha Cordelia, A.; Kokatnoor, Sujatha Arun; Lamani, Manjunath Ramanna; Das, Shreyashi
Lecture Notes in Networks and Systems;Volume;1672 LNNS;pp.205-217
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.1007/978-981-95-3492-0_16 https://www.scopus.com/pages/publications/105040568086?origin=resultslist
Swetha Cordelia A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India; Kokatnoor S.A., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India; Lamani M.R., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India; Das S., Department of Computer Science and Engineering, School of Engineering and Technology, Christ University, Karnataka, Bengaluru, India
Isolation and characterization of plant growth promoting bacteria (PGPB) from the rhizosphere of Spinacea oleracea L.
Abiotic stress; Halotolerance; Pesticide; Plant Growth Promoting Bacteria (PGPB); Tolerance
As the years pass by, there is an increase in abiotic stress conditions around the environment that directly or indirectly affect agriculture around the world. Therefore, there is a dire need to increase the sustainability of plants. Plant Growth Promoting Bacteria (PGPB) play an important role in maintaining the physiology and growth of plants under various stress conditions. This study looks into the isolation and characterization of different PGPB from Spinacia oleracea L. and their tolerance against salinity and commonly used commercial pesticides against the Spinacia family. The techniques used are isolation by serial dilution, 16sRna sequencing, characterization of different PGPB assays for confirmation such as ammonia production, catalase test, phosphate solubilisation, potassium solubilization, siderophore production, indole-3-acetic acid production, biofilm formation assay, halotolerance and tolerance study using Minimal Inhibitory Concentration (MIC). PGPB were isolated and characterized from Spinacia oleracea L., which was under an abiotic stress environment. Isolates were Bacillus clarus, Bacillus licheniformis, Paenibacillus alvei SJ6 and Paenibacillus alvei SJ8, having quantities as high as 78.10.004 mgL-1 phosphate solubilization, 43.8 mgL?1 of indole-3-acetic acid production, 14.5660.011 psu of siderophore production and 0.62 0.027 mol mL?1 of ammonia production. All isolates also had considerable amounts of halotolerance up to 10%, whereas Bacillus licheniformis had 12.5% halotolerance. The bacterial isolates had considerable tolerance against commonly used commercial pesticides against green leafy vegetables such as chlorpyriphos + cypermethrin combination and fungicides such as mancozeb. Therefore, this study looks into the isolation of potential plant growth promoting bacteria that have considerable amount of halotolerance and pesticide tolerance. 2025 World Researchers Associations. All rights reserved.
Research Journal of Biotechnology, Vol-20, No. 1, pp. 77-87.
https://doi.org/10.25303/201rjbt077087 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85214442012&doi=10.25303%2f201rjbt077087&partnerID=40&md5=fdfdb7957ac622c0fdc194a14d38f5b3
Swetha J., Department of Life Sciences, Christ University, Hosur Road, Karnataka, Bangalore, 560029, India; Sayantan D., Department of Life Sciences, Christ University, Hosur Road, Karnataka, Bangalore, 560029, India
MHD Maxwell nanofluid flow over a porous conical surface: A fractional approach
Finite difference method; Fractional model; Maxwell nanofluid; Relaxation times
The current novel study focuses on the two-dimensional magnetohydrodynamic flow of fractional Maxwell nanofluid through porous conical geometry under convective boundary conditions. The nanofluids considered for the study are suspensions of single and multi-walled carbon nanotubes with blood as the base fluid. Fractional-ordered governing equations are transfigured into non-dimensional forms using appropriate transformations. The finite difference approximations are obtained by discretizing the momentum and energy profiles. The results of both profile are plotted against various physical flow-pertaining parameters. It is evident, that multi-walled carbon nanotubes consistently show higher velocity profiles and lower temperature phases than single-walled carbon nanotubes nanofluid across all embedded parameters. Further, the study revealed that the absence of magnetic parameter improves by 11.36% of velocity distribution and the presence of heat source parameter improves by 18.37% of temperature distribution. This framing highlights the convergence criterion of the findings with previous work, emphasizing both reliability and accuracy within the range of 10?4 to 10?6. Graphical representation concludes that the model involving the fractional technique is superior to the integer one. Thus, achievement demonstrates practical application potential in optimizing the efficiency of fluid heating and cooling processes, underscoring its importance in thermal management. 2025
Swetha, D.S.; Madhura, K.R.; Babitha; Badruddin, Irfan Anjum; Kamangar, Sarfaraz; Khan, Abdul Azeem
Results in Engineering;Volume;26;Issue;;Article No.;104853;
All Open Access; Gold Open Access; Green Open Access
https://doi.org/10.1016/j.rineng.2025.104853 https://www.scopus.com/pages/publications/105002493152?origin=resultslist
Swetha D.S., Department of Mathematics, PES University, Karnataka, Bangalore, 560085, India; Madhura K.R., Post Graduate Department of Mathematics, The National College, Jayanagar, Karnataka, Bangalore, 560070, India, Trans - Disciplinary Research Centre, National Degree College, Basavanagudi, India, The Florida International University, United States; Babitha, Department of Mathematics, CHRIST University, Karnataka, Bangalore, 560029, India; Badruddin I.A., Mechanical Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia; Kamangar S., Mechanical Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia; Khan A.A., Faculty of Islamic Technology, University Islam Sultan Sharif Ali, Negara, Brunei Darussalam
Research Journal of Biotechnology;Volume;20;Issue;1;pp.77-87
https://doi.org/10.25303/201rjbt077087 https://www.scopus.com/pages/publications/85214442012?origin=resultslist
Study of cognitive adaptiveness of isolated Plant Growth Promoting Bacteria in nutritionally stress condition
Cognitive adaptability; Memory; Nutritional stress; Plant Growth Promoting Bacteria (PGPB)
The biological processes behind bacterial memory in different species are still under terra incognita. Additionally, the ability of learning through association in prokaryotes is still unknown. Cross-fertilization between the study of multicellular creatures' cognitive capacities and that of bacteria is possible. Therefore, Plant Growth Promoting Bacteria (PGPB) can be used to analyze this cognitive adaptation of bacteria under stress because PGPB is crucial to the maintenance of plant physiology and growth under a variety of stress scenarios. This study focuses on analyzing preliminary evidence of cognitive adaptability in PGPB under nutritional stress conditions. The isolated PGPB were treated with nutritional deprivation in both periodical and non-periodical manners and their performance was compared with the control group. The characteristics of PGPB, such as ammonia production, siderophore production, phosphate solubilization and indole-3-acetic acid, as well as anti-oxidant activities such as DPPH activity, hydroxyl radical scavenging activity and hydrogen peroxide scavenging activities, were analysed and compared to periodically and non-periodically stressed PGPB with control. In the isolated PGPB post-nutrition deprivation treatment, it was evident that the periodically stressed performed better than the non-periodically stress-exposed PGPB compared to the control wherein the isolates produced as high as 2.5510 mol mL-1 ammonia, 23.0406 mgL?1 indole-3-acetic acid, 69.16 0.71 psu siderophore and 123.5780.429mgL-1 phosphate solubilised. Out of the four isolated PGPB, the two novel strains, Paenibacillus alvei SJ6 and Paenibacillus alvei SJ8, have shown to possess the supreme ability to adapt to periodic nutritional stress compared to the other isolates in our study. 2025 World Researchers Associations. All rights reserved.
Research Journal of Biotechnology;Volume;20;Issue;7;pp.114-124
https://doi.org/10.25303/207rjbt1140124 https://www.scopus.com/pages/publications/105007353140?origin=resultslist
The Adoption of AR and VR Emergency Room Procedures
Swetha, N.; Madhavi, K.; Kalebar, Rajiv U; Nallasivam, Anitha; Selvaraj, C.
Virtual Reality, Real Emergency: Integrating AR/VR in Computing and Medical Crisis Management;pp.141-157
https://doi.org/10.1201/9781003564904-10 https://www.scopus.com/pages/publications/105023579410?origin=resultslist
Swetha N., Department of Management, Alliance Ascent College, Alliance University, Bengaluru, India; Madhavi K., Department of Commerce, Maharani Women's Arts, Commerce and Management, Kudumula, India; Kalebar R.U., School of Business and Management, Christ University, Bengaluru, India; Nallasivam A., FMS, CMS Business School, JAIN (Deemed-to-be University), Bengaluru, India; Selvaraj C., Jain Online, JAIN (Deemed-to-be University), Bengaluru, India
Synthetic Data Augmentation for Robust Solar Flare Classification: A Comparative Analysis of Conditional DCGAN, VAE, and Diffusion Models
cDCGAN; Class Imbalance; Conditional Diffusion Model; Conditional Generative Models; cVAE; Data Augmentation; SDG Goal 9; Solar Flare Classification; Synthetic Data
Solar flares are extremely dangerous to the ground and space-based resources. Solar flares have to be classified properly and in good time to offer protection to assets in both the environments. Deep Learning-based flares have been divided into 3 classes (C, M and X). The main problems with this kind of classification are that high impact M- or X-class solar flares are extremely rare, and cannot be well sampled, thus leading to a very unbalanced sample. This paper exemplifies a comparative analysis of three models of Conditional Generative Models (cDCGAN models, cVAE models and cDDPM) to produce realistic images of solar flares considering each of the low frequency and high-impact solar flare types. The research question will be how such models can be evaluated in terms of their capacity to create realistic, class-specific images (magnetograms, and EUV) and time-series data which could be used to create class-balanced training samples. The initial experiments make use of the cDCGAN, cVAE, cDDPM architecture and considering the generation of class-conditional solar flare images portray high levels of stability (convergence was stated in less than 600 epochs in the case of the cDCGAN, cVAE models and cDDPM) and the generation of images which could be considered as practically indistinguishable to real life images. The results indicate that cDDPM may be a possible solution to a high-fidelity production of solar features. We measured the efficiency of these models in quantitative terms (popular metrics, like the Frhet Inception Distance, Structural Similarity Index) in a manner that we could determine the best manner of training model based solar flare classification systems using realistic data. This research is aligned with Sustainability Development Goals 9- Industry, Innovation and Infrastructure with focus on verticals 9.1 and 9.5. 2026 IEEE.
Swetha, S.; Prakash, Gladis Sam; Thomas, Jestin; Kalpana, P.; Jose, Teena; Sumathi, P.
Proceedings of 2nd International Conference on Visual Analytics and Data Visualization, ICVADV 2026;pp.896-901
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICVADV67766.2026.11470543 https://www.scopus.com/pages/publications/105037458658?origin=resultslist
Swetha S., Christ (deemed to be University), Department of Computer Science, Karnataka, Bengaluru, India; Prakash G.S., Christ (deemed to be University), Department of Computer Science, Karnataka, Bengaluru, India; Thomas J., Christ (deemed to be University), Department of Computer Science, Karnataka, Bengaluru, India; Kalpana P., Christ (deemed to be University), Department of Computer Science, Karnataka, Bengaluru, India; Jose T., Christ (deemed to be University), Department of Computer Science, Karnataka, Bengaluru, India; Sumathi P., Vyasa College, Department of Computer Science, Tamil Nadu, Salem, India
Mind set in mathematics learning role of teacher student interaction on student engagement wellbeing and achievement /
Mind-Set In Mathematics Learning : Role of Teacher-Student Interaction on Student Engagement, Wellbeing and Achievemrent
Mathematics learning is an integral part of the school curriculum. Children learn basic concepts in mathematics and then gradually reach the abstract level. Challenges in mathematics learning are largely observed after grade seven. Students may show disinterest towards the subject due to several reasons including past learning experiences, teacher-student interaction (TSI), anxiety and self-efficacy levels. If the students cannot connect what they learn, it impacts their interaction in the classroom, and it acts as a reason for losing interest. The literature review reveals the importance of mathematics anxiety, self-efficacy, and utility value and contribute to the construct of mind-set. Student engagement is influenced by mind-set in mathematics learning and TSI, and predicts achievement and wellbeing. The study adopts a mixed-method design with the qualitative study aiming to support the quantitative study and strengthen the validity of the results. The quantitative study sample consists of 774 eighth graders from various English medium schools in Bengaluru, Karnataka. The qualitative phase seeks to determine the students' perception of mathematics learning through their classroom experiences among 17 students using semi-structured interviews. The tested conceptual model shows an excellent fit. It shows mind-set in mathematics affects TSI, influences student engagement and leads to student-wellbeing. There was no indirect effect for the achievement and other variables. The findings related to the open-ended questions indicate the importance of teachers and content. There is a lack of understanding among students about the practical application of the learning content. The thematic analysis results provided five main themes: student attributes, teacher attributes, classroom environment, content-related and utility value. Integration of the findings leads to the importance of TSI and student engagement in the mathematics classroom. Also, the connection between variables related to mathematics learning and student wellbeing. The results of the study have important implications for developing engaging pedagogies.
Seasonal study on the Aquatic and Terrestrial Habitat of Edayar region, Ernakulam, Kerala, India
Diversity Indices; Plant diversity; Quadrat Study; Random sampling; Species diversity
This study examines the plant diversity and physicochemical characteristics of both aquatic and terrestrial ecosystems in the industrialized region of Edayar, Kadungalloor, Ernakulam, Kerala, India. The research is conducted seasonally, encompassing the four seasons of Kerala: southwest monsoon, northeast monsoon, winter season and summer season. Edayar is home to approximately 400 industries. The main objective of this study is to assess the plant diversity with a specific focus on herb and macrophyte diversity, in the Edayar region, along with analyzing the physicochemical properties of soil and water. Random sampling using quadrat techniques is employed to collect data on species diversity. Diversity indices, such as the Simpson Index and Shannon-Wiener index are utilized to analyze the recorded species diversity. Scoparia dulcis L. among herb species and Eichhornia crassipes (Mart.) Solms among macrophytes were found dominating in all the seasons. The results for the physico-chemical analysis of water and soil were found approaching the threshold of standard limits.The findings provide valuable insights into plant diversity and ecological dynamics of the Edayar region, which have been significantly impacted by industrial activities. The outcomes serve as a basis for the development and implementation of effective conservation and management strategies to mitigate potential ecological risks associated with industrial activities in the region. 2024 World Researchers Associations. All rights reserved.
Research Journal of Chemistry and Environment, Vol-28, No. 11, pp. 82-98.
https://doi.org/10.25303/2811rjce082098 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207397803&doi=10.25303%2f2811rjce082098&partnerID=40&md5=d8ab4232a2d5ba5205ae1ffebce6cc55
Syamlal C.A., Department of Life Sciences, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India; Sayantan D., Department of Life Sciences, Christ (Deemed to be University), Karnataka, Bengaluru, 560029, India
Accumulation of heavy metals (Cr, Cu, As, Cd, Pb, Zn, Fe, Ni, Co) in the water, soil and plants collected from Edayar Region, Ernakulam, Kerala, India
ICP-MS; industrialisation; toxicity; urbanization
The accumulation of heavy metals in the environment is a significant concern due to their potential toxicity and persistence. This study investigates the levels of heavy metal contamination in the water, soil and plants of the Edayar region in Ernakulam, Kerala, India. The region has experienced industrialization and urbanization, leading to concerns about heavy metal pollution. The study aims to assess the concentrations of chromium (Cr), copper (Cu), arsenic (As), cadmium (Cd), lead (Pb), zinc (Zn), iron (Fe), nickel (Ni) and cobalt (Co) in water, soil, aquatic and terrestrial plants. Samples were collected from various locations within the Edayar region, and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was conducted to quantify heavy metal concentrations. The findings of this study will contribute to the assessment of heavy metal pollution in the Edayar region. Plants with a high diversity index were taken for analysis from both aquatic and terrestrial habitats. Scoparia dulcis L. seems to specialize in metal accumulation, possibly for protective purposes. Synedrella nodiflora Gaertn demonstrates adaptability to metal-rich environments through robust metal uptake and tolerance mechanisms. Alternanthera philoxeroides (Mart.) Griseb, on the other hand, appears to have developed mechanisms to manage heavy metal exposure. The results indicate significant levels of heavy metal contamination across all samples, with the highest concentrations detected in soil, followed by water and plants. Chromium and lead levels in soil exceeded the permissible limits set by international standards, posing potential risks to human health and the ecosystem. The accumulation patterns in plants varied, with higher bioaccumulation factors observed for zinc and copper, suggesting their preferential uptake. This study highlights the urgent need for remediation strategies and continuous monitoring to mitigate the impact of heavy metal pollution in the Edayar region. The results will help in understanding the environmental impact of human activities. Copyright: The Author(s).
Plant Science Today, Vol-11, No. 4, pp. 907-923.
https://doi.org/10.14719/pst.3385 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207806084&doi=10.14719%2fpst.3385&partnerID=40&md5=5c4e083282e789bdf0a99ac753f23e3b
Syamlal C.A., Department of Life Sciences, Christ University, Karnataka, Bengaluru, 560029, India; Sayantan D., Department of Life Sciences, Christ University, Karnataka, Bengaluru, 560029, India
Unraveling the Interplay Between Biodiversity and Heavy Metal Content in Elookkaras Aquatic and Terrestrial Ecosystems
Biodiversity; bioremediation; heavy metals; Inductively Coupled Plasma Mass Spectrometry (ICP-MS)
Background and Objective: There exists a notable correlation between biodiversity and the concentration of heavy metals, particularly concerning their role in bioremediation efforts. This study was about the heavy metal content in the aquatic and terrestrial ecosystem of Eloorkkara, located in the Kadungalloor Grama Panchayat of Kerala, India. Materials and Methods: Sampling was systematically carried out across all four seasons in order to capture the fluctuations in seasonal disturbances. Eight samples each of groundwater, river water, aquatic soil and terrestrial soil were randomly collected from the study area. Additionally, three dominant plant species from both aquatic and terrestrial habitats were carefully selected for analysis. Utilizing Inductively Coupled Plasma Mass Spectrometry (ICP-MS), the samples underwent thorough analysis to measure the levels of Cr, Cu, As, Cd, Pb, Zn, Fe, Ni and Co concentrations. Results: Indicate significant differences in heavy metal concentrations across various plant species and throughout seasonal changes, emphasizing the complex processes involved in metal accumulation. Terrestrial ecosystems exhibited higher species richness compared to aquatic ecosystems. Areas with high biodiversity tended to have lower metal concentration suggesting a potential mitigating effect of diverse ecosystems and areas with poor diversity had higher heavy metal concentration suggesting the vulnerability of degraded ecosystems. Conclusion: The research highlights the crucial role of biodiversity in influencing the absorption and dispersion of heavy metals within ecosystems. These findings carry significant implications for environmental management and conservation efforts aimed at curbing heavy metal pollution and safeguarding biodiversity in Elookkara and analogous environments. 2024 Chandni Asha Syamlal and D. Sayantan.
Asian Journal of Plant Sciences, Vol-23, No. 3, pp. 321-329.
Asian Network for Scientific Information
Application of Regression Analysis of Student Failure Rate
Machine learning; Regression; Student failure
The education sector has been rapidly growing and is currently facing several challenges. One such challenge is identifying students who are at risk of failing, as this can help educators provide targeted interventions to improve student performance. Machine learning models have been developed to predict the probability of student failure based on various student performance metrics to address this issue. In this paper, we present a regression-based model that predicts the probability of student failure using student performance metrics such as attendance, previous academic performance, and demographic information. The model was trained on a dataset of students and achieved high accuracy in predicting the probability of student failure. While the model performs well in predicting the probability of student failure, there is always room for improvement. Possible enhancements to the model include feature engineering, ensemble learning, hyperparameter tuning, deep learning, and interpretability. These enhancements can improve the models accuracy, stability, and transparency, leading to better predictions and targeted interventions for at-risk students. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
Lecture Notes in Networks and Systems, Vol-994 LNNS, pp. 897-910.
Sybol S.S., CHRIST (Deemed to be University), Delhi NCR, India; Srivasatava S., CHRIST (Deemed to be University), Delhi NCR, India; Sharma S., Uttaranchal University, Dehradun, India
Soft Computing Approach for Student Dropouts in Education System
Education; Educational technology; Soft computing
The education system has increased the number of dropouts in the coming years, decreasing the number of educated people. Education system refers to a group of institutions like ministries of education, local education bodies, teacher training institutes, universities, colleges, schools, and more whose primary purpose is to provide education to all the people, especially young people and children in educational settings. The research aims to improve the student dropout rate in the education system by focusing on students performance and feedback. The students dropout rate can be calculated based on complexity, credits, attendance, and different parameters. This study involves the extensive study that inculcates student dropout with their performance and other parameters with soft computing approaches. There are various soft computing approaches used in the education system. The approaches and techniques used are sequential pattern mining, sentimental analysis, text mining, outlier decision, correlation mining, density estimation, etc. The approaches and techniques will be beneficial to calculating and decreasing the rate of dropout of students in the education system. The research will make a unique contribution to improved education by calculating the dropout rate of students. In particular, we argue that the dropout rate is increasing, so soft computing techniques can be the solution to improvise/reduce the dropout rate. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Lecture Notes in Networks and Systems, Vol-547, pp. 325-335.
Sybol S.S., CHRIST (Deemed to be University), NCR, Delhi, India; Srivastava S., CHRIST (Deemed to be University), NCR, Delhi, India; Sharma H., Sheffield Hallam University, Sheffield, United Kingdom
Investigation of Diesel Engine Performance, Emissions and Combustion Characteristics Utilizing Emulsified Biodiesel at Varied CRs
Combustion; Emissions; Emulsified biodiesel; Environmental pollutants; Performance
In this experimental investigation, a varied CR (CR) diesel engine is fuelled with palmyra biodiesel B20 (20% palmyra methyl ester + 80% diesel) and emulsified palmyra biodiesel (85% B20 + 10% water + 5% surfactant), with span 80 and tween 80 (hlb of 6.43) used as surfactants. The study aims to evaluate the performance, emissions and combustion characteristics of the engine at varying CR of 17, 17.5 and 18 with the standard CR set at 17.5. Results show that increasing the CR leads to an improvement in Brake Thermal Efficiency (BTE), with a 3.89% higher BTE observed at a CR of 18 compared to 17. Additionally, higher CRs result in significant reductions in emissions, including hydrocarbons by 25.49%, carbon monoxide by 28.35% and smoke by 11.82% compared to running on neat diesel. These findings highlight the potential of emulsified palmyra biodiesel at higher CRs to improve the engine efficiency and reduce emissions, emphasizing its viability as a sustainable alternative fuel. 2025. Carbon Magics Ltd.
Syed, Karimulla; Venkatesulu, M.; Birada, Lakshmi Mansa; Sharon, Gentapogu; Samad, Shaik Abdul; Kumar, Veeranki Sundar; Reddy, V. S. V. S. Ram; Krishna, S. Ganesh Venkata
International Journal of Vehicle Structures and Systems;Volume;17;Issue;2;pp.238-242
https://doi.org/10.4273/ijvss.17.2.10 https://www.scopus.com/pages/publications/105013487245?origin=resultslist
Syed K., Dept. of Mech. Engg, Koneru Lakshmaiah Education Foundation, Deemed to be University, Vaddeswaram, Andhra Pradesh, India; Venkatesulu M., Dept. of Mech. Engg, Vemu Institute of Tech, Andhra Pradesh, Chittoor, India; Birada L.M., Dept. of Mech. Engg., College of Engg, Jawaharlal Nehru Tech. University Kakinada, Andhra Pradesh, Kakinada, India; Sharon G., Dept. of Mech. Engg., College of Engg, Jawaharlal Nehru Tech. University Kakinada, Andhra Pradesh, Kakinada, India; Samad S.A., Dept. of Mech. Engg, Vignans Lara Institute of Tech. and Sci, Andhra Pradesh, Vadlamudi, India; Kumar V.S., Dept. of Mech. Engg, Vignans Lara Institute of Tech. and Sci, Andhra Pradesh, Vadlamudi, India; Reddy V.S.V.S.R., Dept. of Mech. Engg, Vignans Lara Institute of Tech. and Sci, Andhra Pradesh, Vadlamudi, India; Krishna S.G.V., Dept. of Mech. Engg, Vignans Lara Institute of Tech. and Sci, Andhra Pradesh, Vadlamudi, India
Artificial Intelligence in Banking Security-Technical Innovations and Challenges
Artificial Intelligence; Banking Security; Cybersecurity; Machine Learning; Natural Language Processing
The accelerating adoption of artificial intelligence (AI) technologies in the banking sector has introduced transformative possibilities for enhancing security frameworks against increasingly sophisticated cyber threats. This research investigates the technical innovations driven by AI, such as machine learning algorithms, biometric authentication systems, and natural language processing, and their impact on improving fraud detection, cybersecurity monitoring, and compliance automation. The paper identifies how AI systems, through real-time analysis of large-scale transaction data, can locate abnormal behavioral patterns and respond proactively to potential threats, significantly reducing human error and response time. A detailed analysis of the current literature reveals a significant research gap in integrating explainable AI, secure data governance frameworks, and scalable models suited for diverse banking environments. The outcome of this research highlights the need for a balanced approach that fosters technological innovation while addressing regulatory compliance, ethical concerns, and operational constraints, paving the way for a secure and intelligent banking infrastructure. 2025 IEEE.
Syed, Waheeduddin Khadri; Mohammed, Abubakar; Reddy, Janamolla Kavitha; Gupta, Ketan; Logeshwaran, J.
Proceedings of 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2025;pp.170-176
https://doi.org/10.1109/ICICV64824.2025.11085795 https://www.scopus.com/pages/publications/105013074043?origin=resultslist
Syed W.K., University of the Cumberlands, Dept. of Information Technology, Somerville, NJ, United States; Mohammed A., University of the Cumberlands, Dept. of Information Technology, Houston, TX, United States; Reddy J.K., University of the Cumberlands - IT, Hillsborough, NJ, United States; Gupta K., University of the Cumberlands, Dept. of Information Technology, Williamsburg, KY, United States; Logeshwaran J., Christ University, Department of Computer Science, Karnataka, Bangluru, 560029, India
Decentering Religious Metanarratives: A Study of Popular Memes
Social support and help-seeking worldwide
Cross-cultural; Cultural values; Regional; Social support; Subjective social status
Social support has long been associated with positive physical, behavioral, and mental health outcomes. However, contextual factors such as subjective social status and an individuals cultural values, heavily influence social support behaviors (e.g., perceive available social support, accept support, seek support, provide support). We sought to determine the current state of social support behaviors and the association between these behaviors, cultural values, and subjective social support across regions of the world. Data from 6,366 participants were collected by collaborators from over 50 worldwide sites (67.4% or n = 4292, assigned female at birth; average age of 30.76). Our results show that individuals cultural values and subjective social status varied across world regions and were differentially associated with social support behaviors. For example, individuals with higher subjective social status were more likely to indicate more perceived and received social support and help-seeking behaviors; they also indicated more provision of social support to others than individuals with lower subjective social status. Further, horizontal, and vertical collectivism were related to higher help-seeking behavior, perceived support, received support, and provision of support, whereas horizontal individualism was associated with less perceived support and less help-seeking and vertical individualism was associated with less perceived and received support, but more help-seeking behavior. However, these effects were not consistently moderated by region. These findings highlight and advance the understanding of how cross-cultural complexities and contextual distinctions influence an individual's perception, processing, and practice of social support embedded in the changing social landscape. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Szkody E.; Spence A.; do?ru A.; Tushir B.; Chang F.; AKKA? H.; Sotomayor I.; Pavlova I.; Petrovic I.; Norvilitis J.; Pena-Shaff J.; Maney J.; Arrow K.; Rodriguez L.; Moussa-Rogers M.; McTighe M.; Ogba K.T.U.; Yeung S.K.W.A.; Stoppa T.; Yang Y.; Gosnell C.L.; Jie-Brink G.; Van Nostrand J.J.; Arriaga P.; Martin A.; Maksimovic A.; Ursu A.; Karakulak A.; Fitapelli B.; Ashdown B.K.; Sen C.K.N.; Chartier C.; Shane-Simpson C.; Redker C.M.; McKinney C.; Baro D.; Manrique-Millones D.; Reis E.S.; Adamopoulou E.; Volkan E.; Tair E.; Trujillo E.; Kocalar H.E.; Blocker H.; Malik H.; Orta ?.M.; Santos J.C.; Grahe J.; Cuccolo K.; Wignall L.; McLain M.; Kosic M.; Aita M.; Nash M.; Miracle O.O.; Christiano O.; Dimitrova R.; Varma R.; Mann R.; Dhakal S.; Estrada-Villalta S.; Haden S.; Hamilton S.; Camg S.M.; Aljuberi S.; Chin S.; Kohn S.; Verma S.K.; Fletcher T.; Singh T.; Sanders A.; Collado A.; Adusei A.; Itani A.; Kaser A.; Wolfe A.; Stout A.; Akhavan A.; Kirton A.; n-Ero?ul A.R.; Bilir B.; Dupiton C.; Lovett C.; Orsini C.; Kpodo C.; Aceto C.; Redden C.; NyKanen D.; Yildiz D.; Lutringer E.; SevinE.; Baranski E.; Khan F.; Jia F.; Cramariuc G.; Zhang G.; Resulbegoviq H.; Maree H.; Kaur H.; Nelson J.; Espinoza J.S.C.; Hubbard J.; Edlund J.; Protzko J.; Hoang J.; Stork J.; Vasu J.; Salazar J.V.; Myhers K.; Hayward K.; Lu K.; Beardmore L.; Levkiv L.; Godoy L.K.H.; Paulett L.; Gonzalez M.F.B.; Kalantzis M.; Rodrigues M.; varez M.M.; Ott M.; Zlokovich M.; Brosnan M.K.; Mazzaferro M.; Yetkin M.; Johnson M.; Vukelic M.; Clark M.; AlMalik M.; Fedavi N.; Means-Simonsen N.; Cabrera O.; Kovacevic P.; Zhang Q.; Rushing R.; Varakis R.; Richardson R.; Koch S.; Lewis S.; Barrera S.; Zheng S.; Liu S.; Papka S.; Das S.; Ghimire S.; Verma T.; Hillman T.; Ozkusen U.C.; Zhang X.; Gu Y.; Redd B.; Cascalheira C.J.
Current Psychology, Vol-43, No. 22, pp. 20165-20181.
Szkody E., Stony Brook University, Stony Brook, NJ, United States, Stony Brook University, 100 Nicolls Road, Stony Brook, 11790, NY, United States; Spence A., Pace University, New York, NY, United States; do?ru A., kar University, Istanbul, Turkey, Marmara University, ?stanbul, Turkey; Tushir B., Christ College, Bengaluru, India; Chang F., Columbia University, New York, NY, United States; AKKA? H., Ankara Science University, Ankara, Turkey; Sotomayor I., Stony Brook University, Stony Brook, NJ, United States; Pavlova I., Lviv State University, Lviv, Ukraine; Petrovic I., University of Belgrade, Belgrade, Serbia; Norvilitis J., SUNY Buffalo State, Buffalo, NY, United States; Pena-Shaff J., Ithaca College, Ithaca, NY, United States; Maney J., Columbia University, New York, NY, United States; Arrow K., Eastern University, St. Davids, PA, United States; Rodriguez L., Northern Arizona University, Flagstaff, AZ, United States; Moussa-Rogers M., University of South Carolina, Columbia, SC, United States; McTighe M., Rochester Institute of Technology, New York, NY, United States; Ogba K.T.U., University of Nigeria, Nsukka, Nigeria; Yeung S.K.W.A., Columbia University, New York, NY, United States; Stoppa T., Eastern University, St. Davids, PA, United States; Yang Y., Stony Brook University, Stony Brook, NJ, United States; Gosnell C.L., Pace University, New York, NY, United States; Jie-Brink G., William Paterson University, Wayne, NJ, United States; Van Nostrand J.J., Eastern Oregon University, La Grande, OR, United States; Arriaga P., Instituto Universitio de Lisboa, Lisbon, Portugal; Martin A., Rockford University, Rockford, IL, United States; Maksimovic A., University of Donja Gorica, Podgorica, Montenegro; Ursu A., University ?tefan Cel Mare of Suceava, Suceava, Romania; Karakulak A., MEF University, Istanbul, Turkey; Fitapelli B., Columbia University, New York, NY, United States; Ashdown B.K., American University of Sharjah, Sharjah, United Arab Emirates; Sen C.K.N., Ozyegin University, Istanbul, Turkey; Chartier C., Ashland University, Ashland, OH, United States; Shane-Simpson C., University of Wisconsin, Stout, Menomonie, WI, United States; Redker C.M., Ferris State University, Big Rapids, MI, United States; McKinney C., Mississippi State University, Starkville, MS, United States; Baro D., New Mexico State University, Las Cruces, NM, United States; Manrique-Millones D., Universidad Cientica del Sur, Lima, Peru; Reis E.S., ISCTE, Lisbon, Portugal; Adamopoulou E., Business College of Athens, Athens, Greece; Volkan E., Cyprus International University, Nicosia, Cyprus; Tair E., Bulgarian Academy of Sciences, Sofia, Bulgaria; Trujillo E., Northern Arizona University, Flagstaff, AZ, United States; Kocalar H.E., Mu?la S?tk? Koan University, Mu?la, Turkey; Blocker H., Eastern Oregon University, La Grande, OR, United States; Malik H., University of North Carolina, Wilmington, NC, United States; Orta ?.M., Atilim University, Ankara, Turkey; Santos J.C., Central Luzon State University, Nueva Ecija, Philippines; Grahe J., Pacific Lutheran University, Parkland, WA, United States; Cuccolo K., Alma College, Alma, MI, United States; Wignall L., Bournemouth University, Poole, United Kingdom, University of Brighton, Brighton, United Kingdom; McLain M., Ball State University, Muncie Indiana, United States; Kosic M., Scientifc-Cultural Institute Manda, Doberddel Lago, Italy; Aita M., Rochester Institute of Technology, New York, NY, United States; Nash M., Michigan School of Psychology, Farmington Hills, MI, United States; Miracle O.O., Abia State University, Uturu, Nigeria; Christiano O., Central Connecticut State University, New Britain, CT, United States; Dimitrova R., Stockholm University, Stockholm, Sweden; Varma R., Amity University, Noida, India; Mann R., Dalton State College, Dalton, GA, United States; Dhakal S., Tribhuvan University, Kathmandu, Nepal; Estrada-Villalta S., Universidad del Valle de Guatemala, Guatemala City, Guatemala; Haden S., Long Island University, Brooklyn, NY, United States; Hamilton S., Fordham University, New York, NY, United States; Camg S.M., Hacettepe University, Ankara, Turkey; Aljuberi S., California State University East Bay, Hayward, CA, United States; Chin S., University of Louisville, Louisville, KY, United States; Kohn S., Valdosta State University, Valdosta, GA, United States; Verma S.K., Vivekananda College, Delhi, India; Fletcher T., West Liberty University, West Liberty, WV, United States; Singh T., Banaras Hindu University, Varanasi, India; Sanders A., Ashland University, Ashland, OH, United States; Collado A., Ithaca College, Ithaca, NY, United States; Adusei A., Columbia University, New York, NY, United States; Itani A., American University of Sharjah, Sharjah, United Arab Emirates; Kaser A., Ashland University, Ashland, OH, United States; Wolfe A., Columbia University, New York, NY, United States; Stout A., Rockford University, Rockford, IL, United States; Akhavan A., Columbia University, New York, NY, United States; Kirton A., Columbia University, New York, NY, United States; n-Ero?ul A.R., Mu?la S?tk? Koan University, Mu?la, Turkey; Bilir B., Ozyegin University, Istanbul, Turkey; Dupiton C., Columbia University, New York, NY, United States; Lovett C., Columbia University, New York, NY, United States; Orsini C., Columbia University, New York, NY, United States; Kpodo C., Pacific Lutheran University, Parkland, WA, United States; Aceto C., Columbia University, New York, NY, United States; Redden C., Columbia University, New York, NY, United States; NyKanen D., Alma College, Alma, MI, United States; Yildiz D., Ozyegin University, Istanbul, Turkey; Lutringer E., The Chicago School of Professional Psychology, Chicago, IL, United States; SevinE., Ankara Science University, Ankara, Turkey; Baranski E., California State University East Bay, Hayward, CA, United States; Khan F., Ozyegin University, Istanbul, Turkey; Jia F., Seton Hall University, South Orange, NJ, United States; Cramariuc G., University ?tefan Cel Mare of Suceava, Suceava, Romania; Zhang G., Ball State University, Muncie Indiana, United States; Resulbegoviq H., University of Donja Gorica, Podgorica, Montenegro; Maree H., American University of Sharjah, Sharjah, United Arab Emirates; Kaur H., Banaras Hindu University, Varanasi, India; Nelson J., New Mexico State University, Las Cruces, NM, United States; Espinoza J.S.C., Universidad del Valle de Guatemala, Guatemala City, Guatemala; Hubbard J., New Mexico State University, Las Cruces, NM, United States; Edlund J., Rochester Institute of Technology, New York, NY, United States; Protzko J., Central Connecticut State University, New Britain, CT, United States; Hoang J., Columbia University, New York, NY, United States; Stork J., Rockford University, Rockford, IL, United States; Vasu J., Columbia University, New York, NY, United States; Salazar J.V., Ithaca College, Ithaca, NY, United States; Myhers K., Rockford University, Rockford, IL, United States; Hayward K., Rockford University, Rockford, IL, United States; Lu K., Columbia University, New York, NY, United States; Beardmore L., Columbia University, New York, NY, United States; Levkiv L., Lviv State University, Lviv, Ukraine; Godoy L.K.H., Universidad del Valle de Guatemala, Guatemala City, Guatemala; Paulett L., Universidad Cientica del Sur, Lima, Peru; Gonzalez M.F.B., Universidad del Valle de Guatemala, Guatemala City, Guatemala; Kalantzis M., Bowling Green State University, Bowling Green, OH, United States; Rodrigues M., Columbia University, New York, NY, United States; varez M.M., Universidad del Valle de Guatemala, Guatemala City, Guatemala; Ott M., Rockford University, Rockford, IL, United States; Zlokovich M., Psi Chi, Chattanooga, TN, United States; Brosnan M.K., Columbia University, New York, NY, United States; Mazzaferro M., Columbia University, New York, NY, United States; Yetkin M., Bahcesehir University, Istanbul, Turkey; Johnson M., Rockford University, Rockford, IL, United States; Vukelic M., University of Belgrade, Belgrade, Serbia; Clark M., Rockford University, Rockford, IL, United States; AlMalik M., American University of Sharjah, Sharjah, United Arab Emirates; Fedavi N., American University of Sharjah, Sharjah, United Arab Emirates; Means-Simonsen N., Columbia University, New York, NY, United States; Cabrera O., Ithaca College, Ithaca, NY, United States; Kovacevic P., University of Belgrade, Belgrade, Serbia; Zhang Q., Columbia University, New York, NY, United States; Rushing R., Rockford University, Rockford, IL, United States; Varakis R., Business College of Athens, Athens, Greece; Richardson R., Columbia University, New York, NY, United States; Koch S., Long Island University, Brooklyn, NY, United States; Lewis S., Ashland University, Ashland, OH, United States, University of Alabama, Tuscaloosa, United States; Barrera S., Ball State University, Muncie Indiana, United States; Zheng S., Columbia University, New York, NY, United States; Liu S., Columbia University, New York, NY, United States; Papka S., Long Island University, Brooklyn, NY, United States; Das S., Amity University, Noida, India; Ghimire S., Tribhuvan University, Kathmandu, Nepal; Verma T., Columbia University, New York, NY, United States; Hillman T., Rockford University, Rockford, IL, United States; Ozkusen U.C., Ozyegin University, Istanbul, Turkey; Zhang X., Columbia University, New York, NY, United States; Gu Y., Columbia University, New York, NY, United States; Redd B., New Mexico State University, Las Cruces, NM, United States; Cascalheira C.J., New Mexico State University, Las Cruces, NM, United States