SIGNIFICANCE OF NURTURING PERMA FLOURISHING IN HIGHER EDUCATION: AN INTEGRATIVE REVIEW
subject: PERMA; Positive psychological interventions; Promotion of student well-being
description: This integrated review explored the significance of PERMA, a multidimensional well-being framework, and PERMA-based interventions in promoting student well-being within higher education contexts. The literature search resulted in 16 studies, and the synthesizing of key research findings supports the effectiveness of PERMA-based intervention on students overall well-being. The interventions centered on cultivating PERMA (positive emotions, engagement, relationship, meaning, accomplishment) offered as semester courses, classroom-based curricula, or intervention programs were found successful in improving wellbeing, happiness, life satisfaction, motivation, relationship building, engagement in learning, and reducing negative emotions, stress, academic boredom, anxiety, depression. Overall, the review findings demonstrate that embedding a PERMA-based well-being program as a holistic approach in education would foster a supportive learning environment and social connection in promoting individual and collective well-being among the students. Future studies could strengthen the present findings and respond to the limitations of the existing studies, which would provide a better understanding of the application and effects of PERMA-based programs. Copyright: The Author(s).
creator: Kulandaiammal, Rosy; Neelakantan, Meera; Sophia, Rosilin Dalphine
source: Hellenic Journal of Psychology;Volume;22;Issue;1;pp.53-82
publisher: Psychological Society of Northern Greece
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 17901391;
format: online
language: English
type: Article
coverage: Kulandaiammal R., Christ University, Bengaluru, India; Neelakantan M., Christ University, Bengaluru, India; Sophia R.D., Christ University, Bengaluru, India

EXPLORING THE REGULATION OF CHARACTER STRENGTHS: A SCOPING REVIEW ON BALANCING UNDERUSE, OVERUSE, AND OPTIMAL USE FOR WELL-BEING AND PERFORMANCE
subject: Balanced Strength Utilization Model (BSUM); Character strengths; Positive psychology; Strengths imbalance; Well-being
description: Character strengths play a fundamental role in psychological well-being, resilience, and personal development. Traditionally, research in positive psychology has focused on the benefits of character strengths, emphasizing their role in enhancing life satisfaction, fostering positive relationships, and promoting professional success. However, recent studies highlight the critical importance of balance in strength utilization. Specifically, underuse, overuse, and optimal use of strengths can lead to varying consequences across different life domains. This scoping review systematically examined the imbalance of character strengths and its impact on mental health, leadership, and social interactions. A systematic search was conducted across Google Scholar, PubMed, Scopus, and Web of Science, following PRISMA-SCR guidelines. The findings revealed that underuse of strengths is associated with low engagement, passivity, and decreased motivation, while overuse can lead to rigidity, interpersonal conflicts, and burnout. The review further identified contextual moderators (e.g., personality traits, cultural factors, and situational demands) that influence how strengths are applied in everyday life. Based on the findings, this review proposes the Balanced Strength Utilization Model (BSUM), integrating theoretical perspectives and empirical evidence to offer a comprehensive framework for character strength regulation. Future research should focus on longitudinal studies and intervention-based approaches to help individuals optimize strength balance and apply their strengths effectively across different domains. The Author(s). All articles are licensed under the terms and conditions of the Creative Commons Attribution 4.0 International License (CC-BY 4.0 http://creativecommons.org/licenses/by/4.0/).
creator: Giri, Madhu; Pathak, Swati
source: Hellenic Journal of Psychology;Volume;22;Issue;2;pp.141-159
publisher: Psychological Society of Northern Greece
date: 01-01-2025
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 17901391;
format: online
language: English
type: Article
coverage: Giri M., CHRIST University, India; Pathak S., CHRIST University, India

Edge criticality in signed graphs admitting a Roman dominating function
subject: roman dominating function; roman domination edge critical graphs; roman domination number; Signed graphs
description: A Roman dominating function(RDF) on a signed graph S = (G, ?) is a function f: V (S) ? {0, 1, 2} such that f(N[v]) ? 1 for every vertex v ? V (S) and any vertex v with f(v) = 0 has a neighbour u ? N + P (v) having f(u) = 2, where f(N[v]) = f(v) + ?u?N(v) ?(uv)f(u). The weight of an RDF is ?(f) = ?v?V f(v) and the minimum weight among all the RDFs on S is called the Roman domination number, ?R(S). In this article we explore the concept of edge criticality in signed graphs admitting an RDF by examining the signed graphs S such that ?R(S+uv) < ?R(S), for any pair of non-adjacent vertices u and v of S, such that the edge uv is positive. This work is licensed under https://creativecommons.org/licenses/by/4.0/
creator: Joseph, James; Joseph, Mayamma
source: Art of Discrete and Applied Mathematics;
publisher: University of Primorska
date: 01-01-2025
rights: All Open Access; Gold Open Access
relation: ISSN: 25909770;
format: online
language: English
type: Article
coverage: Joseph J., CHRIST (Deemed to be University), Bangalore, 560029, India; Joseph M., CHRIST (Deemed to be University), Bangalore, 560029, India

BERT-Enhanced Bi-LSTM with weighted cross-entropy for multilingual sentiment classification
subject: BERT; Bi-LSTM; Contextual embeddings; Low-Resource languages; Multilingual sentiment analysis
description: With the increasing volume of multilingual user-generated content across social media platforms, effective sentiment analysis (SA) becomes crucial, especially for low-resource languages. However, traditional models relying on context-independent embeddings, such as Word2Vec, GloVe, and fastText, struggle to handle the complexity of multilingual sentiment classification. To address this, we propose an Automatic Multilingual Sentiment Detection (AMSD) framework that leverages the contextual capabilities of BERT for feature extraction and a Bidirectional Long Short-Term Memory (Bi-LSTM) network for classification. Our method, termed Elite Opposition Cross-Entropy Weighted Bi-LSTM (EOCEWBi-LSTM), integrates elite opposition-based learning to optimize hyperparameters and enhance classification accuracy. A weighted cross-entropy loss function further refines the model's sensitivity to class imbalance, thereby improving its performance. The model is trained and evaluated on the NEP_EDUSET corpus, comprising 45,434 tweets in English, Hindi, and Tamil. Experimental results demonstrate notable improvements in precision, recall, F1-score, and accuracy, highlighting the effectiveness of EOCEWBi-LSTM in multilingual sentiment analysis, especially across both high-resource and low-resource languages. The experimental results show that the proposed EOCEWBi-LSTM achieves a high F1-score ratio of 93.83% and an accuracy ratio of 93.83% compared to other existing methods. EOCEWBi-LSTM provides an effective solution for multilingual sentiment analysis, especially for languages with limited resources. 2025 The Author(s).
creator: Siddique, Mohammad Mustafa; Kumar, Sandeep
source: International Journal of Advances in Intelligent Informatics;Volume;11;Issue;3;pp.396-416
publisher: Universitas Ahmad Dahlan
date: 01-01-2025
rights: All Open Access; Gold Open Access; Green Open Access
relation: ISSN: 24426571;
format: online
language: English
type: Article
coverage: Siddique M.M., Christ University, Bengaluru, 560074, India; Kumar S., Christ University, Bengaluru, 560074, India

Precise cervical cancer cell boundary denoising and segmentation with adaptive wavelet-spectral enhancement
subject: Adaptive wavelet-spectral; Cell boundary denoising; Cervical cancer; Cervical cells; Segmentation enhancement
description: Accurate segmentation of cell nuclei in cervical cytology images is crucial for automated cervical cancer screening, yet existing methods struggle with blurred boundaries, noise-induced degradation, and topologically implausible predictions. The current research proposes Cell-Seg Tool, a novel triplet-branch diffusion AI tool that synergistically integrates three innovations to address these limitations. The Wavelet-Enhanced Contour Refinement Branch employs a learnable multi-scale discrete wavelet transform with adaptive coefficient attention to dynamically enhance boundary features across horizontal, vertical, and diagonal orientations. The Adaptive Spectral Noise Suppression module performs dual-domain processing using DCT-based filtering and uncertainty-guided fusion, coupled with bidirectional anchor semantic feedback to couple cross-branch information. The Topology-Aware Hybrid Loss integrates a focal Tversky loss, a persistent homology loss, a directional boundary loss, a skeleton completeness loss, and a diffusion-noise MSE loss for multi-objective optimization. Comprehensive experiments on multiple datasets demonstrate superior performance, achieving 94.45% Dice coefficient and 19.2% reduction in boundary localization error compared to state-of-the-art methods. Unlike prior work that applies these techniques independently, this work demonstrates that their adaptive, synergistic integration within a diffusion-based framework yields substantial improvements in boundary accuracy and topological correctness. 2026 The Author(s).
creator: Mukku, Lalasa; Lamani, Manjunath Ramanna; Hegde, Lavanya; Mahapurush, Prathima; Mahapurush, Shivanandaswamy
source: International Journal of Advances in Intelligent Informatics;Volume;12;Issue;1;pp.136-164
publisher: Universitas Ahmad Dahlan
date: 01-01-2026
rights: All Open Access; Gold Open Access; Green Open Access
relation: ISSN: 24426571;
format: online
language: English
type: Article
coverage: Mukku L., CHRIST (Deemed to be University), Bangalore, 560074, India; Lamani M.R., Moodlakatte Institute of Technology, Kundapura, 576217, India; Hegde L., GOVERNMENT SKSJTI, Bangalore, 560001, India; Mahapurush P., SKSVMACET Gadag, India; Mahapurush S., Moodlakatte Institute of Technology, Kundapura, 576217, India

COSMOLOGICAL DIAGNOSTICS OF BIANCHI TYPE-II BARROW HOLOGRAPHIC DARK ENERGY UNIVERSE; [????????I??? ?I????????? ????????I???? ?????? ?????I? ?????I?? ?????? ???? ?I???I-II]
subject: Barrow holographic dark energy; Bianchi type-II model; Cosmology; Dark energy; Modified theory of gravity
description: In this paper, we investigate a Bianchi type II anisotropic cosmological model in the framework of Barrow holographic dark energy, considering both the Hubble horizon and GrandaOliveros scale as infrared cutoffs. To obtain exact solutions of the Einstein field equations, we assume a suitable relation between the metric potentials. Using Hubble cosmic chronometer data, we constrain the model parameters and obtain the best-fit values b4= ?0.091+0.013 ?0.012and H0= 72.32.7 km s?1Mpc?1The H(z) fit shows excellent agreement with observational data and overlaps with ?CDM at low redshifts, with mild deviations at higher z. The physical behaviour of the model is examined through a detailed analysis of cosmological parameters. The deceleration parameter q(z) reveals a smooth transition from an early decelerating phase to the present accelerating epoch. The equation of state parameter ?deshows quintom-like dynamics, evolving across the cosmological constant boundary and entering the phantom regime, consistent with late-time acceleration. Stability is tested using the squared sound speed vs2, which remains positive in the recent Universe, ensuring classical stability. The ?de?dephase plane indicates that both models lie in the freezing region, corresponding to faster acceleration. The statefinder diagnostics (r,s) and (r,q) further confirm the transition from the standard cold dark matter dominated phase to a de Sitter-like attractor, with trajectories showing clear deviations from ACDM. U.Y.D. Prasanthi, D. Tejeswararao, Diddi Srinivasa Rao, Y. Aditya, D. Ram Babu, 2026.
creator: Prasanthi, U.Y. Divya; Tejeswararao, D.; Srinivasa Rao, Diddi; Aditya, Y.; Ram Babu, D.
source: East European Journal of Physics;Volume;2026;Issue;1;pp.60-75
publisher: V N Karazin Kharkiv National University
date: 01-01-2026
rights: All Open Access; Gold Open Access; Green Open Access
relation: ISSN: 23124334;
format: online
language: English
type: Article
coverage: Prasanthi U.Y.D., Department of Statistics & Mathematics, College of Horticulture, Dr. Y.S.R. Horticultural University, Parvathipuram, 535502, India; Tejeswararao D., Department of Basic Sciences and Humanities, GMR Institute of Technology (GMRIT), Deemed to be University, Rajam, 532127, India; Srinivasa Rao D., Department of Mathematics, Aditya University, Surampalem, 533437, India; Aditya Y., Department of Mathematics, GMR Institute of Technology (GMRIT), Deemed to be University, Rajam, 532127, India; Ram Babu D., Department of Mathematics, GMR Institute of Technology (GMRIT), Deemed to be University, Rajam, 532127, India

An Efficient Fuzzy Logic-Integrated Hybrid Deep Learning Framework for Medical Diagnosis
subject: adaptive neuro-fuzzy inference system (ANFIS); decision support systems; deep learning; fuzzy logic; intelligent healthcare systems; medical diagnosis
description: Medical diagnosis involves analyzing symptoms, test results, and patient histories, but uncertainty from vague symptoms and incomplete records complicates the process. Fuzzy logic-based systems address this issue but often depend on manual rule creation, which is time-consuming. This research proposes a hybrid approach integrating fuzzy logic with deep learning techniques (FL-DLT) for intelligent diagnosis. The framework combines adaptive neuro-fuzzy inference system (ANFIS) for handling uncertainty with convolutional neural networks (CNNs) for extracting features from medical images like X-rays and MRIs. ANFIS models relationships between symptoms, results, and diagnoses, while CNNs analyze medical images. Experimental results show high accuracy and reliability, even with noisy or incomplete data. The proposed approach can improve diagnostic accuracy and efficiency, supporting clinicians in decision-making. Key contributions include the development of the FL-DLT framework and its evaluation using a large dataset of patient records and medical images. Additionally, the research offers insights into the application of fuzzy logic and deep learning in medical diagnosis, highlighting their potential to enhance diagnostic outcomes and efficiency in clinical practice. 2009 Tsinghua University Press.
creator: Saranya, R.; Rajagopal, Manikandan; Ramprasath, J.; ThamaraiSelvi, K.; Leelavathy, S.
source: Fuzzy Information and Engineering;Volume;18;Issue;1;pp.1-18
publisher: Tsinghua University Press
date: 01-01-2026
rights: All Open Access; Gold Open Access
relation: ISSN: 16168658;
format: online
language: English
type: Article
coverage: Saranya R., Dr. SNS Rajalakshmi College of Arts and Science, Department of Computer Science with Cyber Security, Coimbatore, 600049, India; Rajagopal M., School of Business and Management, Christ University, Bangalore, 600871, India; Ramprasath J., Dr. Mahalingam College of Engineering and Technology, Department of Information Technology, Pollachi, 642003, India; ThamaraiSelvi K., New Horizon College of Engineering, Department of Information Science and Engineering, Bangalore, 600671, India; Leelavathy S., Panimalar Engineering College, Department of AI&DS, Chennai, 600123, India

Fuzzy Computational Intelligence in Personalized Medicine and Diagnosis
subject: computational intelligence; diabetes management; fuzzy logic (FL); medical decision support; personalized medicine; treatment optimization
description: The development of fuzzy computational intelligence (FCI) has emerged as an effective method for personalized medicine and diagnosis. FCI effectively handles uncertainty and imprecision in medical data, facilitating patient-specific treatment recommendations. Conventional diagnostic and treatment methods typically rely on fixed threshold-based approaches, which fail to account for individual variations in patient responses, leading to suboptimal treatment outcomes. This study proposes the personalized treatment recommendation using fuzzy logic (PTR-FC) framework for diabetes (DB) patients to address these challenges. The framework integrates patient-specific data such as blood glucose levels, diet, exercise, and medication history into the fuzzy inference system (FIS), supporting personalized treatment recommendations. The treatment plans are dynamically adapted based on individual patient outcomes using linguistic factors and fuzzy rules (FR). The proposed method dynamically adjusts recommendations in real time, potentially enhancing personalized treatment and improving decision-making in DB management. Additionally, it promotes lifestyle modifications while reducing the risk of medication-induced complications. The effectiveness of the proposed method was compared to conventional methods, demonstrating improved treatment accuracy, increased patient adherence, and reduced adverse health risks. The PTR-FC framework offers a more adaptive and effective approach to DB management, ensuring better patient outcomes. 2009 Tsinghua University Press.
creator: Bavirthi, Swathi Sowmya; Manikandan, M.; Rajagopal, Manikandan; Leelavathy, S.; Rao, K. Srinivas
source: Fuzzy Information and Engineering;Volume;17;Issue;4;pp.472-483
publisher: Tsinghua University Press
date: 01-01-2025
rights: All Open Access; Gold Open Access
relation: ISSN: 16168658;
format: online
language: English
type: Article
coverage: Bavirthi S.S., Chaitanya Bharathi Institute of Technology, IT Dept., Hyderabad, 500075, India; Manikandan M., School of Computing, SRM Institute of Science and Technology, Faculty of Engineering and Technology, Department of Computational Intelligence, Chennai, 603203, India; Rajagopal M., School of Business and Management, Christ university, Bangalore, 560029, India; Leelavathy S., Department of AI&DS, Panimalar engineering college, Chennai, 503200, India; Rao K.S., MLR Institute of Technology, Department of Computer Science and Engineering, Hyderabad, 722003, India

https://archives.christuniversity.in/items/show/23445
title: Impact of Ionic Liquids on the Crystal Growth and Surface Morphology of Ruthenium-doped TiO2 Nano Heterojunction Structures for Improved Photocatalytic Degradation of Evans Blue Dye and the Associated Antibacterial Activities
subject: antibacterial activity; hydrothermal method; ionic liquid; photocatalytic degradation; Ru/TiO2 nanocomposite
description: Novel Ru-doped TiO2 nanocomposites (Ru/TiO2 NCs) were synthesized at a 130 C temperature and 24-h incubation period using hydrothermal methods with and without ionic liquids (ILs). NCs were synthesized using 1-butyl-2,3-dimethylimidazolium tetrafluoroborate as the ILs and titanium(IV) isopropoxide and ruthenium(III) nitrate as the precursors. The presence of Ru in the NCs was analyzed using different characterization techniques. Powder X-ray diffraction and transmission electron microscopy confirmed the presence of anatase and rutile phases as well as the nanocrystalline texture of the prepared Ru/TiO2 NCs. The presence of Ru, Ti, and O was confirmed via energy-dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy. The optical properties and bandgap energies of Ru/TiO2 NCs were determined via ultraviolet (UV)visible (Vis) diffuse reflectance spectroscopy; the optical properties exhibited a redshift in the optical response toward the visible region owing to the reduced bandgap energy of Ru/TiO2 NCs in the visible region after doping Ru into the TiO2 nanocrystalline structure. Scanning electron microscopy images revealed a highly voluminous and porous network of Ru/TiO2 NCs. Moreover, different concentrations of Ru were doped into the TiO2 matrix to investigate the photocatalytic and antibacterial activities. Among all the synthesized NCs, 0.3-wt% Ru/TiO2 NCs exhibited high photocatalytic degradation efficiency and was therefore considered the optimum concentration. Moreover, it exhibited the highest BET surface area and quantum efficiency compared with other Ru/TiO2 NCs. Results revealed that Ru/TiO2 NCs synthesized via IL-assisted hydrothermal method, i.e., R3TL, exhibited considerably enhanced photocatalytic and antibacterial activities compared to the NCs synthesized without the ILs, i.e., R3TH. The inhibition pattern showed an excellent zone of inhibition (p < 0.001) in several strains with both NCs (R3TL and R3TH). However, Gram-positive Staphylococ…
creator: Ravishankar, T.N.; Ananda, A.; Adarsha, J.R.; Shilpa, B.M.
source: Nano Biomedicine and Engineering;Volume;17;Issue;4;pp.570-588
publisher: Tsinghua University Press
date: 01-01-2025
rights: All Open Access; Gold Open Access; Green Open Access
relation: ISSN: 21505578;
format: online
language: English
type: Article
coverage: Ravishankar T.N., Department of Chemistry, B.M.S. College of Engineering, Karnataka, Bangalore, 560019, India; Ananda A., Department of Chemistry, Dayananda Sagar Academy of Technology & Management Udayapura, Bengaluru, 560082, India; Adarsha J.R., Department of chemistry, Global Academy of Technology, Ideal Homes Township, Karnataka, Bangalore, 560098, India; Shilpa B.M., Department of Psychology, Christ-Deemed to be University, Bangalore Kengeri Campus, Karnataka, Bangalore, 560074, India

BRICS VS. G7: A COMPARATIVE ANALYSIS OF ECONOMIC AND POLITICAL EFFICIENCY IN SHAPING GLOBAL ORDER
subject: Comparison; Efficiency; Geopolitical; Multipolar; Power-Dynamics
description: The global distribution of power is increasingly shaped by the competing influences of two major blocs: BRICS (Brazil, Russia, India, China, and South Africa) and the G7 (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States). This paper investigates how BRICS and the G7 shape the emerging multipolar global order. Using comparative analysis of key indicators: GDP, trade flows, investment patterns, diplomatic engagement, and strategic alliances. The paper examines each blocs structure and internal cohesion. The analysis underscores the G7's historical supremacy, which stems from its economic strength and political unity, in contrast to BRICS rising role as a representative for the Global South and a platform for alternative governance models. Important metrics include trade flows, investment trends, diplomatic efforts, and strategic alliances. The research also assesses the internal dynamics within each bloc, including challenges to cohesion and the effectiveness of decision-making. By comparing the advantages and drawbacks of BRICS and G7, this paper provides insights into their respective functions in a multipolar world order, evaluating their ability to promote transformative global agendas. Lastly, the paper concludes that both alliances embody divergent approaches to global governance, reflecting deeper shifts in international collaboration, competition, and the balance of power. 2025, Observare. All rights reserved.
creator: Dilip, Patkar Kalpesh
source: Janus.net;Volume;16;Issue;2;pp.290-306
publisher: Observare
date: 01-01-2025
rights: All Open Access; Gold Open Access
relation: ISSN: 16477251;
format: online
language: English
type: Article
coverage: Dilip P.K., Nyaya in the Shanti Parva of the Mahabharata, India, Christ (Deemed to be University), Lavasa Campus, Pune, India

Understanding the drivers of intermittent fasting adoption in middle adulthood
subject: Intermittent fasting; Metabolism; Middle age; Regimen
description: Over the past decade, intermittent fasting has gained into the mainstream limelight as a prevalent method for weight management and to maintain metabolic health. However, the number of studies exploring the determinants affecting its approval, especially among individuals in middle adulthood, is still comparatively under-examined. Hence, the present study has been conducted to gain better insights about the factors influencing the adoption of intermittent fasting among middle-aged adults. By employing snowball sampling, a sample of 260 respondents was surveyed to comprehend the drivers of intermittent fasting, challenges faced in adhering to it and its impact on physiological and psychological health. Factor analysis was employed to group the factors influencing the respondents adherence to intermittent fasting into three categories: longevity-enhancing incentives for intermittent fasting, wellness incentives for intermittent fasting and cognitive enhancement incentives for intermittent fasting. In order to examine the significant differences among the ages of the respondents among the three factors, ANOVA and post-hoc test were conducted. The post-hoc test results provide insight into how motivational factors for intermittent fasting vary across different age groups. For respondents in the age group between 35 to 45 years, the post-hoc results show statistically significant differences in the first factor with the group aged 18-25 and 25-35 years, respectively at 5 % level of significance. The insights garnered from this research are contributory in understanding the factors influencing how and why individuals in middle adulthood embrace intermittent fasting practices. 2025 The Authors.
creator: Rizwana, M.; Pallavi, B.; Deepak, R.; Muralidhar, S.; Dheepa, T.
source: Food Research;Volume;9;Issue;5;pp.80-86
publisher: Rynnye Lyan Resources
date: 01-01-2025
rights: All Open Access; Gold Open Access
relation: ISSN: 25502166;
format: online
language: English
type: Article
coverage: Rizwana M., Department of Management Studies, M.S. Ramaiah Institute of Technology, Karnataka, Bengaluru, 560054, India; Pallavi B., Department of Management Studies, M.S. Ramaiah Institute of Technology, Karnataka, Bengaluru, 560054, India; Deepak R., Department of Management Studies, M.S. Ramaiah Institute of Technology, Karnataka, Bengaluru, 560054, India; Muralidhar S., Department of Management Studies, B.M.S College of Engineering, Karnataka, Bengaluru, 560019, India; Dheepa T., School of Business and Management, Christ University, Karnataka, Bengaluru, 560029, India

FEATURE SELECTION AND CLASSIFICATION OF LEUKEMIC CELLS USING IOT AND MACHINE LEARNING
subject: Diagnosing; Feature Selection; IoT; Leukemic Cells; Machine Learning; Understanding
description: Machine learning and the Internet of Things (IoT) have affected every step of the leukemia process, from diagnosis to understanding to therapy. Consequently, this study delves into the planning of an innovative system that employs IoT and machine learning techniques to precisely differentiate leukemic cells. Depending on the patient's samples, the system uses different ways to feature selection and cell classification. To pick the most informative collection of features that enables stable and accurate cell categorization into suitable categories, the offered research relies on strong machine-learning approaches for feature selection. Next, a classification model is used to classify cells based on their properties using the attributes that have been chosen. There is evidence that the suggested approach can classify leukemic cells with an identification rate of up to 99%, which is greater than the current methods. As a novel strategy for managing massive volumes of biological and medical samples, the suggested method will be an invaluable tool for doctors treating leukemia patients. The system's ability to process data from various Internet of Things (IoT) sources should aid its ability to learn and adapt to real-world clinical settings. With the results of this study in hand, we may be able to detect leukemia sooner, with greater precision, and maybe use more tailored treatments for each patient, leading to better results while reducing healthcare expenditures. 2025, Institute of Mechanics of Continua and Mathematical Sciences. All rights reserved.
creator: Vineetha, K.R.; Bhargavi, Kovvuri N.; Narasamba Vanguri, G.L.; Mahilraj, Jenifer; Kannan, V.
source: Journal of Mechanics of Continua and Mathematical Sciences;Volume;20;Issue;3;pp.36-53
publisher: Institute of Mechanics of Continua and Mathematical Sciences
date: 01-01-2025
rights: All Open Access; Gold Open Access
relation: ISSN: 9738975;
format: online
language: English
type: Article
coverage: Vineetha K.R., Department of Computer Science, Christ University, Karnataka, Bengaluru, 560029, India; Bhargavi K.N., Department of Information Technology, Aditya College of Engineering and Technology, Surampalem, India; Narasamba Vanguri G.L., Department of Information Technology, Aditya College of Engineering and Technology, Surampalem, India; Mahilraj J., Department of AI & DS, NPR College of Engineering and Technology, Dindigul, Natham, India; Kannan V., CLDC Research and Development, No.997, Mettupalayam Road, Near X-CutSignal, R.S.Puram, Tamil Nadu, Coimbatore, India

PERFORMANCE EVALUATION OF IPE AND IE-AFFECTED PATIENTS USING A MODIFIED PSO AND ANFIS
subject: ANFIS; Idiopathic Epilepsy (IE); Idiopathic Partial Epilepsy (IPE); Modified Particle Swarm Optimization (MPSO)
description: Epilepsy, a complex neurological disorder, is particularly challenging to diagnose and manage when driven by genetic factors. This study focuses on the analysis of Idiopathic Partial Epilepsy (IPE) and Idiopathic Epilepsy (IE) in both children and women, using a novel approach combining Modified Particle Swarm Optimization (MPSO) with a 9-rule Adaptive Neuro-Fuzzy Inference System (ANFIS). Four feature extraction techniquesDiscrete Wavelet Transform (DWT), Shearlet Transform (SLT), Contourlet Transform (CLT), and Stockwell Transform (SWT)are employed to process electroencephalogram (EEG) signals. The performance of the proposed MPSO-ANFIS model is evaluated and compared with existing methods. Results indicate that the SWT-ANFIS-MPSO method achieves superior classification accuracy for both IE and IPE patients, highlighting its potential to improve epilepsy diagnosis and treatment strategies. 2025, Institute of Mechanics of Continua and Mathematical Sciences. All rights reserved.
creator: Swain, Kaliprasanna; Tak, Tan Kuan; Upreti, Kamal; Kshirsagar, Pravin R.; Krishnan, Sivaneasan Bala; Poonia, Ramesh Chandra; Mohapatra, Sumant Kumar; Nayak, Sumya Ranjan
source: Journal of Mechanics of Continua and Mathematical Sciences;Volume;20;Issue;6;pp.65-89
publisher: Institute of Mechanics of Continua and Mathematical Sciences
date: 01-01-2025
rights: All Open Access; Gold Open Access
relation: ISSN: 9738975;
format: online
language: English
type: Article
coverage: Swain K., Department of ETC, Trident Academy of Technology, Bhubaneswar, India; Tak T.K., Singapore Institute of Technology, Singapore; Upreti K., CHRIST University, Delhi NCR, Ghaziabad, India; Kshirsagar P.R., J. D. College of Engineering & Management, Maharashtra, Nagpur, India; Krishnan S.B., Singapore Institute of Technology, Singapore; Poonia R.C., CHRIST University, Delhi NCR, Ghaziabad, India; Mohapatra S.K., Department of ETC, TAT, Odisha, Bhubaneswar, 24, India; Nayak S.R., School of Computer Engineering, KIIT University, Bhubaneswar, India

Financial Freedom, social Capital, and the Development of Rural Women Entrepreneurship in India; [?????????? ???????, ?????????? ??????? ? ???????? ??????????????????? ????? ???????? ?????? ? ?????]
subject: entrepreneurship; gender inequality; rural region; SPSS; women entrepreneurs
description: Human resource development can only be achieved by promoting female entrepreneurship. There is a very low level of female entrepreneurship in India, especially in rural areas, which has recently been a cause for concern. Women are now aware of their existence, privileges, and employment circumstances.The subject of this research is female entrepreneurs in rural India, their contribution towards society, problems faced by women entrepreneurs in India, and initial steps taken by the administration for their development in Indias rural region. The research is explanatory. The primary data is used in the paper. The self-structured questionnaire was circulated to the women entrepreneurs in rural India. The data collected was analysed using a targeted sampling method in the Statistical Package for Social Sciences programme, followed by a study of the statistical results. During the survey, 44 respondents were interviewed. The results showed that among the most significant challenges were womens family responsibilities, gender inequality, financial difficulties, low risk inclination and competition between men and women. It was concluded that the challenges faced by women entrepreneurs could be addressed through appropriate incentives, training, encouragement, social recognition of their entrepreneurial capabilities and appropriate family support. Victor M., Elangovan N., Halaswamy D., Sonia M., 2024.
creator: Victor, Melvin; Elangovan, N.; Halaswamy, Devla; Sonia, Mathew
source: Finance: Theory and Practice;Volume;29;Issue;6;pp.93-106
publisher: Financial University under The Government of Russian Federation
date: 01-01-2025
rights: All Open Access; Gold Open Access; Green Open Access
relation: ISSN: 25875671;
format: online
language: English
type: Article
coverage: Victor M., School of Business and Management, CHRIST University, Bengaluru, India; Elangovan N., School of Business and Management, CHRIST University, Bengaluru, India; Halaswamy D., School of Business and Management, CHRIST University, Bengaluru, India; Sonia M., School of Business and Management, CHRIST University, Bengaluru, India

TRANSFER LEARNING TECHNIQUES AND APPROACHES FOR PREDICTIVE MODELING OF DISEASE OUTCOMES
subject: deep attention mechanisms; dengue prediction; healthcare informatics; predictive an-alytics; transfer learning
description: Aim/Purpose In this research work, we have developed a predictive model that focuses on utilizing knowledge from the related domains. Background A serious public health issue, especially in tropical and subtropical regions, is dengue fever, a viral infection passed by mosquitoes. Accurate early prediction of disease outcomes is essential for both efficient patient management and ef-fective use of resources. More complex methods are required since conven-tional prediction models could be faulty with limited labeled data and complex feature interactions. Methodology We propose a new strategy integrating deep attention mechanisms with trans-fer learning to enhance prediction modeling of dengue disease outcomes. First pre-trained on a large, linked dataset of common viral illnesses, a deep neural network enables the model to learn generic properties. We then iteratively im-prove our pre-trained model using a specific dengue dataset. Incorporating a deep attention mechanism allows for the focus on the most relevant features, improving interpretability and accuracy. Contribution Among logistic regression, random forests, and basic deep learning methods, current models reveal poor accuracy and dependability in forecasting dengue disease outcomes. These models sometimes fail to sufficiently depict the com-plicated interactions among clinical variables, especially under conditions with limited data. Findings The proposed method outperforms more traditional models pretty strongly. Our model acquired in the training phase an accuracy of 0.92, precision of 0.91, recall of 0.90, and F1-score of 0.90. It maintained high performance on testing with an accuracy of 0.91, precision of 0.90, recall of 0.89, and an F1-score of 0.89. Similar patterns were indicated by an accuracy of 0.90, precision of 0.89, recall of 0.88, and an F1-score of 0.88 validation results. The model also demonstrated a lowered loss (0.21, 0.23, 0.24 in training, testing, and vali-dation, respectively), higher …
creator: Mahalaxmi, S.B.K.U.; Kala, I.; Sagar, Lalit Kumar; Thomas, N.; Kaushal, Ashish Kumar; Robert, Nismon Rio
source: Informing Science;Volume;28;Issue;;Article No.;Article 16;
publisher: Informing Science Institute
date: 01-01-2025
rights: All Open Access; Gold Open Access
relation: ISSN: 15479684;
format: online
language: English
type: Article
coverage: Mahalaxmi S.B.K.U., Department of Electronics and Communication Engineering, Aditya College of Engineering and Technology, Andhra Pradesh, Surampalem, India; Kala I., Department of Computer Science and Engineering, PSG Institute of Technology and Applied Research, Coimbatore, India; Sagar L.K., Department of Computer Science and Engineering, SRM Institute of Science and Technology, Delhi-NCR Campus, Modinagar, Uttar Pradesh, Ghaziabad, India; Thomas N., Department of Chemistry, St Mother Theresa Engineering College, Vagaikulam, Tamil Nadu, Thoothukudi, India; Kaushal A.K., Department of Jindal Global Business School, O P Jindal Global University, Haryana, Sonepat, India; Robert N.R., Department of Computer Science, Christ University, Bangalore, India

THE INFLUENCE OF STEM ATTITUDE, 21ST CENTURY SKILLS, AND TECHNOLOGY USE ON STUDENT OUTCOMES: A MEDIATION MODEL ANALYSIS
subject: 21st century skills; Indian adolescents; STEM career interest; structural equation modeling; student engagement; subjective well-being; teacher efficacy; teacher leadership; technology use
description: Aim/Purpose This study aims to investigate the relationships between students attitudes toward STEM subjects, 21st-century skills, and technology use, and how these factors influence STEM career interest and subjective well-being among adolescents in the 21st-century classroom. Background While positive attitudes, skill acquisition, and digital learning have been individually studied, their combined effect on students career aspirations and subjective well-being remains underexplored. Addressing this gap can show how these factors work together to support adolescent development. Methodology This study was grounded in established theories of learning and motivation. It employed a statistical method, Structural Equation Modeling (SEM), to test a conceptual model. Data were collected from 1,302 students (grade levels VIII-XII) across 30 schools in Kerala, India. Also analysed were the mediating effects of mathematics and science engagement, teacher efficacy, and teacher leadership. Contribution This research offers a combined model that reveals how students attitudes, skills, technology use, and classroom teaching practices are connected. These factors are shown to influence both career motivation and well-being. Together, the findings provide a broader and clearer picture of modern education. Findings Students with positive attitudes toward STEM showed more engagement in learning. Effective and supportive teachers also influenced them. This influence shaped their career interest. Their well-being was improved indirectly through this engagement and career motivation, but their direct impact on well-being was limited. The model demonstrated good fit indices, supporting its structural validity. By addressing the gap in integrative models that link learner attitudes, competencies, technology use, and instructional mediators to both career and well-being outcomes, this study contributes a holistic framework for understanding adolescent development in modern classrooms. Rec…
creator: Nair, Kavitha Radha; Kareem, Jacqueline
source: Journal of Information Technology Education: Research;Volume;24;Issue;;Article No.;29;
publisher: Informing Science Institute
date: 01-01-2025
rights: All Open Access; Gold Open Access
relation: ISSN: 15479714;
format: online
language: English
type: Article
coverage: Nair K.R., Christ University, Karnataka, Bengaluru, India; Kareem J., Christ University, Karnataka, Bengaluru, India

FROM CLASSROOMS TO CLICKS: EXPLORING STUDENT ATTITUDES AND CHALLENGES IN THE SHIFT TO DIGITAL LEARNING IN HIGHER EDUCATION
subject: digital learning; higher education; online assessments; online classroom engagement
description: Aim/Purpose This study is a cross-sectional survey that explores attitudes towards online classroom engagement, online assessments, exams, and challenges in digital learning. Background Educational institutions have adopted digital platforms with varying degrees of success. The COVID-19 pandemic has prompted researchers and academics to reflect on digital interventions and their impact on pedagogy, learning, and assessment. However, in the present circumstances, online teaching, learning, and assessment will require learners to bring their own ethics, unrestrained by external institutional rules. Methodology The sample consisted of 1,017 students in higher education across India. The researchers developed a tool, the Digital Learning Scale, which was built on three factors: attitude towards online classroom engagement, attitude towards online assessments, and challenges in digital learning. This tool was used to collect data. Contribution The study results prove the effect of online learning and online assessments in higher education institutions. Findings The significant findings of this study are: (1) approximately 50% of the students prefer that there should be a balance between online and offline teaching, learning and evaluation; (2) there is significant positive high correlation between the last online exam and last offline exam scores; (3) there is significant positive correlations between attitudes towards online class engagement, attitude towards online assessments and exams and challenges in digital learning; (4) 65% of the students agree that digital learning increases social isolation; (5) 55% of the students agree that incidence of cheating by the students in online exams is evident. Recommendations Educational institutions should invest in robust strategies to promote academic for Practitioners integrity, provide technical support, and offer training programs that equip students with the necessary digital skills for achieving successful learning outcom…
creator: Kareem, Jacqueline; Venkatesh, Arathi; Abhaya, N.B.
source: Journal of Information Technology Education: Research;Volume;24;Issue;;Article No.;31;
publisher: Informing Science Institute
date: 01-01-2025
rights: All Open Access; Gold Open Access
relation: ISSN: 15479714;
format: online
language: English
type: Article
coverage: Kareem J., Christ University, Karnataka, Bengaluru, India; Venkatesh A., Christ University, Karnataka, Bengaluru, India; Abhaya N.B., Christ University, Karnataka, Bengaluru, India

RESPONSIBLE AI-READINESS IN HIGHER EDUCATION: VALIDATING A DUAL-MODEL FRAMEWORK FOR FACULTY DEVELOPMENT
subject: AI-readiness; artificial intelligence; dual-model validation; faculty development
description: Aim/Purpose This study validates a responsible artificial intelligence (AI) framework designed to strengthen AI-readiness among higher education faculty in India. Background With the increasing use of AI in education, faculty require structured support; however, faculty development models for responsible AI integration remain limited. Methodology A mixed-methods pilot study with ten humanities and performing arts faculty used a dual-model evaluation approach. Three sessions from the framework were implemented, and data were collected through knowledge-attitude-practice (KAP) surveys, cognitive-affective-psychomotor (CAP) performance rubrics, and reflective responses. Contribution This study validates a dual-model framework for faculty development regarding responsible AI readiness. Findings The adapted KAP survey demonstrated strong reliability, with higher AI knowledge associated with more positive attitudes. However, knowledge did not consistently translate into practice, highlighting the need for structured hands-on learning. CAP-based performance assessments and reflections indicated improved ethical awareness, critical engagement, and foundational AI-integration skills. Recommendations for Practitioners Institutions should embed structured AI-training for faculty, with authentic instructional tasks. Recommendations for Researchers Future research should test this approach across larger and more diverse institutional contexts. Impact on Society Developing AI-ready faculty can foster ethical and future-focused learning environments. Future Research Future studies should expand across disciplines and examine longer-term outcomes. (2025), (Informing Science Institute). All rights reserved.
creator: Rawat, Sanskriti; Ashton-Bell, Robert Linton Tavis
source: Journal of Information Technology Education: Research;Volume;24;pp.1-20
publisher: Informing Science Institute
date: 01-01-2025
rights: All Open Access; Gold Open Access
relation: ISSN: 15479714;
format: online
language: English
type: Article
coverage: Rawat S., Christ University, Bangalore, India; Ashton-Bell R.L.T., Christ University, Bangalore, India

Analysis of skip-lot sampling plan of Type 3 with multiple reference criteria
subject: acceptance sampling plan; average sample number; operating characteristic curve; quality control; skip-lot sampling plan
description: In the modern quality control segment, the skip-lot sampling plan is still significant among all others plans due to rising production volumes and the demand for cost-effective inspection methods that will yield high-quality outputs. Unlike other sampling plans, while inspecting a submitted lot, a skip-lot plan is economically advantageous and ensures high quality. The skip-lot sampling plan utilizes single sampling plan (SSP) or double sampling plan (DSP) as the reference plan during both normal and skipping inspections. However, using these plans as the reference can lead to bias, favouring either the producer or the consumer. In this paper, a novel approach is illustrated where the skip-lot sampling plan of type 3 is having the provision of two different reference plans in the normal and skipping phases. The proposed plan is termed as the Multi-Reference Skip-lot Sampling Plan of type 3 (MR-SkSP-3). The plan is then compared with the help of performance measures such as operational characteristic (OC) function and average sample number (ASN). The comparison is done between the proposed plan and existing skip-lot sampling plans which use single sampling plan or double sampling plan as reference plan in both inspection phases. The comparison is made based on performance measures with graphical and tabulated illustrations. The comparative analysis proves that the proposed plan successfully balances the satisfaction of both producers and consumers. By leveraging the strengths of conventional skip-lot sampling plans that use single reference plans, it achieves superior performance. 2026 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved.
creator: Manoj, Meby Joseph; Azarudheen, A.
source: Communications for Statistical Applications and Methods;Volume;33;Issue;2;pp.263-275
publisher: Korean Statistical Society
date: 01-01-2026
rights: All Open Access; Gold Open Access
relation: ISSN: 22877843;
format: online
language: English
type: Article
coverage: Manoj M.J., Department of Statistics & Data Science, CHRIST (Deemed to be University), India; Azarudheen A., Department of Statistics & Data Science, CHRIST (Deemed to be University), India

Peak-to-Average Power Ratio (PAPR) Aware Signal Shaping in UFMC: An Adaptive Orthogonal Precoding and Gaussianized Companding Approach
subject: Adaptive Hybrid Orthogonal Precoding, Gaussianized Companding; Amplifiers; Energy Efficacy; Selective subcarrier companding; Universal Filtered Multi-Carrier
description: Universal Filtered Multi-Carrier (UFMC) is a promising waveform for 5G and beyond wireless systems due to its superior spectral localization and low-latency characteristics; however, its high Peak-to-Average Power Ratio (PAPR) significantly degrades power amplifier efficiency and causes nonlinear distortion and out-of-band (OOB) emissions. To address these limitations, this paper proposes an Adaptive Hybrid Orthogonal Precoding with Gaussianized Companding (AHO-PCG) framework for PAPR-aware UFMC transmission. The proposed approach integrates adaptive orthogonal precoding, Gaussianized Dynamic Companding (GDC), and Selective Subcarrier Companding (SSC) within a joint optimization loop to balance PAPR reduction, signal fidelity, and spectral containment. Gaussianization reshapes the signal amplitude distribution to suppress extreme peaks, while adaptive precoding dynamically selects suitable orthogonal transforms based on signal characteristics, and SSC limits nonlinear processing to dominant peak-contributing subcarriers. Simulation results under 3GPP LTE-compliant parameters using QPSK, 16-PSK, and 64-QAM demonstrate that AHO-PCG achieves up to 4.4 dB PAPR reduction at a CCDF of 10-3, reduces EVM to 2.9%, suppresses OOB emissions to 42 dBm, and improves BER by up to an order of magnitude compared to baseline UFMC. These gains are achieved with a modest processing overhead, making the framework suitable for real-time 5G baseband implementations and extensible to future 6G waveform designs. 2026 Mahendrapublications.com, All rights reserved.
creator: Raja, Ponmani; Sujatha, S.; Prajoon, P.
source: International Journal of Advanced Science and Engineering;Volume;12;Issue;3;pp.6084-6103
publisher: Mahendra Publications
date: 01-01-2026
rights: All Open Access; Bronze Open Access
relation: ISSN: 23495359;
format: online
language: English
type: Article
coverage: Raja P., Department of Electronics & Communication Engineering, Christ (Deemed to be University), Bangalore, 560074, India; Sujatha S., Department of Electronics & Communication Engineering, Christ (Deemed to be University), Bangalore, 560074, India; Prajoon P., Department of Electronics & Communication Engineering, Jyothi Engineering College, Kerala, 679531, India