Telematique

Severity Aware Early Diabetes Prediction Using Hybrid Machine Learning and Deep Learning Techniques

K H Rizwana, Dr. Ajay Sharma — Wed, 10 Dec 2025 00:00:00 +0000

In the recent trend and in medical domain, diabetes stands out as one of the most predominant disease and unfortunately considering as a life-threatening disease. Early prediction of diabetes plays a pivotal role in initiating prompt treatment and halting the progression of the condition. The proposed methodology not only aids in predicting the future diabetes but also finds its severity scores. By presenting this issue as a multi-class classification problem, hybrid machine learning (ML) and deep learning (DL) techniques are used to build the new hybrid model. This helps in incorporating both structural feature learning of ML and deep temporal pattern recognition of DL for better performance. The hybrid ML+DL for diabetes prediction used XGBoost, LightGBM, CatBoost ML models and Temporal Convolutional Network (TCN) as base layer, Logistic Regression (LR) as a meta-classifier. The model is evaluated and fine-tuned for effective diabetes disease prediction with its score of severity. The experimental findings underscore the effectiveness of each component in the framework and its impact on the accuracy. The proposed work proves the sufficient amount of accuracy as 99.4%, and HML+DL compared with the recent studies in prediction of early stage of diabetes.

Mapping Research Themes and Future Directions in Learning Style Detection Research: A Bibliometric and Content Analysis

Ms. Neethu D S, Dr. Ajay Sharma — Mon, 12 Jan 2026 00:00:00 +0000

This study aims to provide a comprehensive overview of the current state and potential future research in learning style detection. With the increasing number and diversity of research in this area, a quantitative approach is necessary to map out current themes and identify potential areas for future research. To achieve this goal, a bibliometric and content analysis will be conducted to map out the existing research and identify emerging topics and directions for future research. The study analyzes 1074 bibliographic sources from Scopus and visualizes the results of the bibliometric analysis through cooccurrence and thematic map analysis using VOSviewer and BibliometriX software. Content analysis is then conducted based on the results of the co-occurrence analysis. The findings reveal a significant increase in publications and citations in the field, with popular research topics including classification, adaptive learning, and MOOCs, and the most frequently used learning style models being Felder-Silverman, VARK, and Kolb. Emerging research topics include the use of EEG signals, online learning, and feature extraction. Future research may focus on classification, intelligent tutoring systems, MOOCs, online learning, adaptive learning, and deep learning. This study provides valuable insights into the current and future research trends in learning style detection, which can support the development of adaptive e-learning systems, intelligent tutoring systems, and MOOCs. By identifying popular research topics and emerging areas of study, this research can guide the design and implementation of effective online learning environments. Additionally, the study advances the field of e-learning knowledge by providing a comprehensive overview of the most frequently used learning style models and potential research areas. It sheds light on the ongoing development of learning style detection research and the potential for future advancements in the field, ultimately contributing to the growth and improvement of e-learning practices.

A study of Radiological Image-Based Bone sarcoma Detection Using Transfer Learning

P. J. Adit, Dr. C. Priya — Thu, 15 Jan 2026 00:00:00 +0000

Bone sarcoma occurs primarily in children, adolescents and adults. Diagnostic assessment has traditionally involved subjective and often time-consuming assessments of imaging modalities such as X-ray, MRI and CT scans. This paper introduces a framework based on deep learning for automated classification of Bone Sarcoma from standard imaging modalities. The modernization utilizes MobileNetV2, a labeled dataset trained on ImageNet, to efficiently extract significant features while limiting computation. Preprocessing of the dataset included image normalization, resizing and augmentation using flipping, rotation, changing the zoom level and adding contrast. The dataset had a split of 80% affected and remaining 20% unaffected, respectively. During the fine-tuning phase the last layers of the model were unfrozen, and models trained at a reduced learning rate to accommodate the imaging data specific to Bone Cancer. The model trained with Adam optimizer and binary cross-entropy loss function with about 93% training accuracy and over 90% validation accuracy. Using evaluations from precision, recall, F1, and confusion matrix, the results verified the model robustness with minimal false negative rates being crucial for medical diagnostic. The results indicated that the suggested approach also provides a reliable, lightweight, and accurate diagnostic support for radiologists.

Optimization of Pumping Station Units Operation

Sat, 17 Jan 2026 00:00:00 +0000

This article provides a complete analysis of the current operational condition of pumping stations and the operating modes of water transmission channels. The most dangerous processes of changing the operating mode at pumping stations caused by a sudden interruption of energy supply to the engine are studied. The issues of using advanced, modern systems during operation of pumping stations, as well as replacing outdated, serviceable equipment at pumping stations with significantly reduced efficiency of economical equipment with new ones are analyzed. A new effective method for determining the optimal operating mode of pumping units has been developed. The obtained results on reducing stress in transient modes of pumping units, adjusting operating modes and optimizing operating modes are analyzed.

Design of Experiment-Based Rp-Hplc Bioanalytical Method Development and Validation for the Estimation of Linagliptin and Dapagliflozin in Bulk and in Their Combined Dosage Form

Vivek B. Tonge, Dr. Pankaj Kapupura, Dr. Hitesh Vekariya — Tue, 27 Jan 2026 00:00:00 +0000

The aim of this study was to develop and optimize a simple, cost-effective, and robust Bioanalytical RP-HPLC method by Design of Experiment based Box–Behnken design for the simultaneous estimation of Linagliptin and Dapagliflozin in human plasma. The optimal chromatographic separation was achieved having C18 (Thermo) column (250 mm × 4.6 mm, 5 μ) and using mobile phase as Methanol and 0.1 % OPA (80:20) with a flow rate 0.9 ml/min and UV detection at 242 nm. A Box-Behnken design was used to test robustness of the method with describes the interrelationship of Mobile phase, Flow rate and Wavelength at three different levels. The method was found to be linear in the range of 2-10 μg/mL (R² >0.9997) and 4-20 μg/mL (R² >0.9994) for Linagliptin and Dapagliflozin, respectively. The developed bioanalytical method was validated as per recommended ICH guidelines which revealed the high degree of linear, precise, accurate, sensitive and robust method over the existing RP-HPLC method for Vildagliptin and Pioglitazone hydrochloride.

A Bibliometric Analysis for UPI Acceptance and Uses for Indian Customers and Business Community

Komal Naroliya, Dr. Anil Sharma — Mon, 02 Feb 2026 00:00:00 +0000

The Unified Payments Interface (UPI) has revolutionized India’s financial landscape, transitioning the nation toward a cashless economy. This study conducts a bibliometric analysis to examine UPI's adoption and usage patterns among Indian customers and businesses. By analyzing 2,560 articles with over 50,000 citations, this research identifies key trends, including user adoption determinants, technological advancements, and socio-economic impacts. It highlights the pivotal role of trust, perceived usefulness, and financial inclusion in driving UPI adoption, while addressing barriers like digital literacy and cybersecurity challenges. Using co-authorship, co-citation, and keyword mapping, this study uncovers thematic clusters and collaborations in global UPI research. Findings underscore UPI's transformative role in fostering financial inclusion, streamlining transactions for micro, small, and medium enterprises (MSMEs), and supporting government-led digital initiatives. This research provides valuable insights into the scholarly evolution of UPI, setting the stage for future explorations into its global scalability and integration with emerging technologies.

Enhancement Algorithms to Improve the Class of Endoscopy Images to Advance Gastrointestinal Track Disease Detection

K. Sharifa, Dr. S. Malarvizhi — Fri, 13 Feb 2026 00:00:00 +0000

Gastro Intestinal (GI) track diseases are major health concern which require accurate detection for early diagnosis and treatment. One of the primary methods of GI track disease detection is to use endoscopic images of digestive track system. However, these images often are degraded by the presence of noise and poor contrast, which directly affect the diagnostic accuracy. Enhancing the quality of the degraded endoscopic images can greatly help physicians during diagnostics and treatment plan. In this work a unified approach which combines denoising and contrast variation correction is proposed. Denosing is executed using a fusion algorithm that pools discrete wavelet transformation and singular value decomposition that is combined with non-local means denoising algorithm. The contrast variation correction is performed using an adaptive gamma correction algorithm enhanced using particle swarm optimization method. Experiments proved that the proposed unified approach improves the visual quality while preserving significant image, edge and structure details. This method can therefore be used safely by GI track disease detection and classification system to improve its diagnostic accuracy.

Networks, Legacies and Continuity In Indian Family Businesses

Mon, 16 Feb 2026 00:00:00 +0000

This study develops an extended theory of change explaining how family firms convert networks into performance. Drawing on an abductive, multiple-case qualitative design with eight Indian family businesses, and using Gioia-style coding, we distinguish what different ties accomplish, when their benefits turn negative, and how organizational scaffolds convert access into repeatable results. The analysis yields eight propositions that are consistent with the evidence: bonding/“as-if-family” density underwrites resilience but exhibits diminishing returns beyond an over-embeddedness threshold; non-kin diversity across industries and geographies predicts growth and co-creation; digital enablement amplifies the payoffs to bridging; governance and learning routines (partner ownership, review cadence, after-action notes) mediate the translation of diversity into scalable pipelines; and institutional bridging (banks, universities, government, associations) complements business networks, particularly in regulated or capital-intensive sectors. Three family-specific levers further refine the account: formalization of AIFB expedites funding decisions, reputation and community trust support price premiums and repeat business, and diaspora ties compress time-to-foreign-entry. We integrate these mechanisms into a four-stage network maturity ladder:S1 Kin-Centred Foundations, S2 Boundary Opening, S3 Structured Brokerage, S4 Orchestrated Partnerships—with observable progression triggers and leading indicators (deal velocity, referral yield, alliance revenue share, time-to-funds, price premium, time-to-foreign-entry). The contribution is a testable mid-range theory that specifies mechanisms and contingencies in family-firm networking while offering a practitioner-ready diagnostic and progression roadmap.

Advanced AI-Driven Techniques for Prostate Cancer Prediction

Kavitha D, Dr. A. S. Aneeshkumar — Mon, 23 Feb 2026 00:00:00 +0000

Prostate Cancer (PCa) is one of the most common causes of malignancy and death in men worldwide, with a higher prevalence and mortality in developing countries. Factors such as age, family history, race and certain genetic mutations are some of the factors contributing to the occurrence of PCa in men. Prostate cancer is a growth of cells that starts in the prostate. Prostate is a small gland placed below the bladder to make semen, as a part of male reproductive system. Prostate cancer is one of the most common types of cancer. Prostate cancer is usually found early, and it often grows slowly. Most people with prostate cancer are cured. People diagnosed with early prostate cancer often have many treatment options. Treatments may include surgery, radiation therapy or carefully watching the prostate cancer to see if it grows. This study aims to review various machine-learning models, which are for the prediction and early detection of prostate cancer using clinical data. Machine-learning focuses on enabling systems to learn from data and improve their performance over time without explicit programming.

Enhanced Multi-Scale Attention 3D Deep Network (EMA-3DNet++) for Segmentation, Classification, and Severity Grading of Knee Injuries

Mr. B. Ramesh Kumar, Dr. R.Padmapriya — Mon, 23 Feb 2026 00:00:00 +0000

Sports-related knee injuries such as anterior cruciate ligament (ACL) tears, meniscal degeneration, and cartilage defects demand precise and early diagnosis to prevent long-term functional impairment. Although deep learning–based medical image analysis has significantly improved automated knee assessment, existing 3D convolutional models often suffer from limited global contextual reasoning, inadequate cross-structure modeling, and poor generalization across heterogeneous clinical datasets. To address these limitations, this research work proposes EMA-3DNet++, a next-generation Federated Multi-Scale Transformer–Graph Hybrid 3D Deep Network designed for accurate, explainable, and privacy-preserving sports knee analysis. EMA-3DNet++ integrates self-supervised pre-trained 3D encoders with multi-scale convolutional feature extraction and Swin Transformer blocks to capture both fine-grained anatomical details and long-range spatial dependencies in volumetric MRI data. A Graph Neural Network (GNN) module explicitly models structural relationships among knee components—ACL, PCL, meniscus, cartilage, tibia, and femur—enhancing multi-label injury classification and structural consistency. To further improve robustness, a hybrid temporal refinement unit incorporating LSTM and energy-efficient spiking neural layers captures motion dynamics in sequential scans. The framework adopts a multi-task learning strategy, simultaneously performing segmentation, injury classification, and severity grading.

To overcome data scarcity and privacy constraints, EMA-3DNet++ supports federated learning with secure aggregation, enabling collaborative training across institutions without data sharing. An advanced explainability layer combining 3D Grad-CAM++, attention rollout, and uncertainty estimation enhances clinical interpretability. Experimental evaluation on benchmark datasets including MRNet, SKI10, and OAI demonstrates that EMA-3DNet++ achieves superior Dice coefficient (94–96%), improved sensitivity and specificity, and enhanced cross-domain generalization compared to state-of-the-art 3D CNN baselines. The proposed framework represents a scalable, clinically deployable, and energy-efficient solution for next-generation AI-assisted sports knee diagnostics.

Study of Professional Hazards in Chefs through Ayurvedic Perspective

Shubham Bibhishan Mote, Sangram Mane — Sat, 10 Jan 2026 00:00:00 +0000

Introduction:
Chefs face unique occupational hazards such as prolonged heat exposure, irregular eating habits, physical strain, and psychological stress. In Ayurveda, these factors correspond to Nidana like Atapa sevana, Ati-ushna-tikshna ahara, Vishama ahara, Vega-dharana, and Manasika nidana, leading to Vata-Pitta aggravation, Agnimandya, Aama formation, and Srotodushti.

Methods:
A narrative review was conducted using PubMed, Google Scholar, Scopus, AYUSH portals, and classical Ayurvedic texts. Literature linking chef occupational hazards with disease outcomes was identified and mapped to Ayurvedic concepts of Nidana, Dosha, and Srotas.

Results:
Occupational hazards identified in chefs included heat and steam exposure, irregular dietary intake, prolonged standing, repetitive strain, and high job stress. In Ayurvedic interpretation, these hazards aggravate Vata and Pitta doshas and vitiate Rasavaha, Annavaha, Mamsavaha, and Manovaha srotas. Modern studies report corresponding conditions such as gastritis, obesity, musculoskeletal disorders, varicose veins, and burnout.

Discussion:
Ayurveda offers preventive strategies including Pitta-pacifying diet, Abhyanga, Sheetali pranayama, Rasayana herbs (Amalaki, Guduchi, Shatavari), and ergonomic modifications. Integrating Ayurvedic measures with occupational safety practices may reduce disease burden and improve overall well-being in chefs.

Therapetic uses of Vidanga Leha in Children W.S.R. Krimi. A Case Series

Baristha Borah, Deepak S. Khawale, Shubham Sharma — Sat, 10 Jan 2026 00:00:00 +0000

Krimi have been considered a major public health problem through out the world according to WHO, 1967. In our country this problem is equally significant. The helminths and bacteria discussed today are somewhat comparable to the Krimi described in ancient texts. According to CCRAS 1987 reports, it affects youngsters more often than adults. Krimi impairs a person's growth and development, causes malnutrition, and lowers immunity; therefore, an efficient remedy to this issue is required. Of all the herbs used in treating worm infestation, Vidanga leha (Embelia ribes) was used for the present study. 30 patients were randomly selected for the study. factors like Vivarnata (Skin Pigmentation), Udarshul (Abd pain ), Gudkandu (itching near anal region), MalaVishtambhata (constipation), Aganimandya (Anorexia) were observed in this study. These clinical conditions had been described by Acharya Charaka under the Lakshana of Krimi and it has been also described in Rasavaha Srotodusti Lakshanas as well. In 15 days of study, observations were recored 0n 0^th, 7^th, and 15^th day and doses were administrated according to body weight of the patients.

Objective: To evaluate the efficacy of vidanga leha in krimi betwenn the age group of 1-12 years.

Conclusion: This study revealed notable alterations in the Krimi patients. Relief had been observed in the various parameters chosen for the study.

Assessment of Moisture Content in the Form of an Indicator of Ahara Samskara Effect

Sat, 10 Jan 2026 00:00:00 +0000

This study aims to scientifically validate the Ayurvedic principle of Ahara Samskara (food processing) by analyzing the changes in the moisture content of Shashtik Shali (a 60-day rice variety) following Kala Samskara (time-based storage) and Agni Samskara (heat processing). The research employs the oven-drying method, a standard technique in modern food science, to quantitatively assess the physicochemical transformations. Freshly harvested Shashtik Shali showed a moisture content of 4.4%. After being stored for eight months and subsequently dry-roasted, the grains' moisture content was reduced to 4.0%. This quantifiable reduction serves as a tangible metric that correlates with the qualitative changes described in Ayurveda. The fresh grain's higher moisture content reflects a predominance of Jala and Prithvi Mahabhutas, making it highly nourishing (Brimhana), but potentially contributing to Ama (toxins) in individuals with weak digestion. The processed grain, with its reduced moisture, reflects a shift towards Agni and Vayu Mahabhutas, acquiring qualities that are lighter (Laghu), drying (Ruksha), and more stimulating to the digestive fire (Dipana), making it therapeutically beneficial for managing conditions like Mandagni (weak digestion) and Kapha Dosha aggravation. The findings bridge ancient Ayurvedic wisdom with contemporary analytical methods, demonstrating that traditional food processing techniques are sophisticated interventions that purposefully modulate a food's properties for specific therapeutic and health-promoting effects.

Artificial Intelligence in Behavioural Finance and Financial Literacy: An Empirical Study on Investor Decision-Making and Financial Awareness

Thu, 26 Feb 2026 00:00:00 +0000

The rapid digitalisation of financial services has significantly altered the way individuals perceive, process, and act upon financial information. Behavioural finance highlights that financial decisions are often influenced by cognitive biases, emotions, and heuristics rather than rational evaluation alone. In recent years, Artificial Intelligence (AI) has emerged as a powerful enabler in addressing these behavioural inefficiencies by offering personalised insights, predictive analytics, and adaptive financial learning tools. Despite the growing adoption of AI-driven financial applications, limited empirical research exists on how AI influences behavioural biases and financial literacy simultaneously, particularly in emerging economies. The present study aims to examine the role of Artificial Intelligence in behavioural finance with specific reference to its impact on financial literacy and investor decision-making. The study identifies key behavioural biases such as overconfidence, loss aversion, herding, and present bias, and analyses how AI-based tools assist individuals in recognising and mitigating these biases. A structured questionnaire-based survey method is proposed to collect primary data from individual investors and working professionals using AI-enabled financial platforms. Descriptive statistics, factor analysis, and regression analysis are suggested to assess the relationship between AI usage, behavioural responses, and levels of financial literacy. The study is expected to contribute to behavioural finance literature by integrating technological intervention into behavioural models of financial decision-making. It offers practical implications for policymakers, fintech developers, and financial educators by highlighting how AI-driven financial tools can promote informed decision-making, enhance financial awareness, and foster long-term financial well-being. The findings may support the development of inclusive and responsible AI-based financial education frameworks.

Solving Bipolar Fuzzy Linear system of Equations: Least Squares Approximation Technique

Nirmala V, Parimala V — Sat, 28 Feb 2026 00:00:00 +0000

An bipolar fuzzy linear system of equations is solved in this article using the least squares approximation technique. Two crisp linear systems are created from the given bipolar fuzzy linear system, and the new systems are expressed in the matrix format. The least squares solutions for each of the systems are separately found out. The bipolar fuzzy linear system's solution is derived from these two solutions. The effectiveness of the suggested technique has been demonstrated with few numerical examples.

AML-BDA: A Sector-Aware Adaptive Multi-Layer Big Data Architecture for Scalable and Intelligent Cross-Industry Analytics

Nandhini Shree J P, Dr. R. Rangaraj — Sat, 28 Feb 2026 00:00:00 +0000

The rapid proliferation of high-volume, high-velocity, and high-variety data across industries has intensified the need for scalable and adaptive Big Data architectures. Traditional distributed frameworks often suffer from static pipeline configurations, limited domain awareness, and inefficient resource utilization, thereby constraining cross-industry analytics performance. This research proposes an Adaptive Multi-Layer Big Data Architecture (AML-BDA) designed to address these limitations through sector-aware abstraction and dynamic orchestration. The proposed framework integrates ontology-driven domain mapping, workload-based auto-scaling, intelligent model selection, and continuous feedback optimization within a unified architecture. AML-BDA introduces a Domain Abstraction Layer to harmonize heterogeneous datasets and an Adaptive Processing Layer that dynamically reconfigures computational resources based on real-time workload metrics. Experimental validation across healthcare, financial fraud detection, smart manufacturing, and retail forecasting datasets demonstrates significant improvements in processing latency, throughput, scalability, and predictive accuracy compared to conventional architectures. Results show latency reduction of up to 35%, throughput enhancement exceeding 20%, and prediction accuracy gains of approximately 5–8%. The findings confirm that integrating sector-aware metadata intelligence with adaptive orchestration mechanisms enhances both operational efficiency and analytical performance. This study contributes a scalable, interoperable, and intelligent Big Data framework capable of supporting next-generation data-driven decision systems across heterogeneous industrial environments.

Obsessive-Compulsive Disorder (OCD): A Comprehensive Review

Krishna Gaikwad, Sachin Kulkarni — Sat, 10 Jan 2026 00:00:00 +0000

Obsessive-compulsive disorder (OCD) represents a chronic neuropsychiatric condition characterized by recurrent intrusive thoughts (obsessions) and repetitive behaviors or mental acts (compulsions) that significantly impair daily functioning and quality of life. Despite substantial advances in understanding its neurobiological underpinnings and therapeutic interventions, OCD remains frequently underdiagnosed, undertreated, and associated with considerable individual and societal burden. This comprehensive review synthesizes current evidence regarding OCD epidemiology, pathophysiology, diagnostic challenges, comorbidity patterns, and evidence-based treatment approaches, with particular emphasis on treatment-resistant populations and emerging therapeutic modalities. A systematic literature search was conducted across PubMed/ MEDLINE, PsycINFO, Scopus, and Google Scholar databases, focusing on peer- reviewed articles published between 2015 and 2025. Search terms included combinations of MeSH headings and keywords related to OCD diagnosis, neurobiology, comorbidities, and treatment interventions. OCD affects approximately 2.3% of the global population across the lifespan, with onset typically occurring in childhood, adolescence, or early adulthood. The disorder demonstrates substantial heritability (approximately 50%) and involves dysregulation within cortico-striatal-thalamic-cortical circuits, with contributions from serotonergic, dopaminergic, and glutamatergic neurotransmitter systems. Comorbidity rates exceed 50% for major depressive disorder and anxiety disorders, while bipolar disorder, schizophrenia spectrum disorders, and attention-deficit/hyperactivity disorder demonstrate prevalence rates of 10-25% in OCD populations. First-line treatments comprising selective serotonin reuptake inhibitors (SSRIs) and cognitive-behavioral therapy with exposure and response prevention (ERP) achieve response rates of 50-70%; however, approximately 30% of patients exhibit treatment resistance, necessitating augmentation strategies, neuromodulation approaches, or neurosurgical interventions. Despite significant therapeutic advances, the substantial proportion of treatment-resistant patients and the complex comorbidity landscape underscore the necessity for continued research into novel treatment targets, personalized medicine approaches, and improved diagnostic methodologies. The integration of neurobiological findings with clinical phenotyping represents a promising avenue for advancing OCD care.

Ethical Artificial Intelligence, Blockchain Transparency and Immersive Digital Technologies for Sustainable Business and Economic Transformation

Priti Mangesh Bharambe, Ashwini Amol Satkar — Sat, 10 Jan 2026 00:00:00 +0000

The fast merging of Artificial Intelligence (AI), Blockchain and Immersive Digital Technologies (AR/VR) is significantly transforming the sustainable business practise and economic systems. However, the issues of algorithmic bias, a lack of transparency and disproportional access to digital access still remain to restrain their responsible and fair usage. This paper hypothesises and empirically testifies to an integrated digital sustainability model that looks at the joint impact of Ethical AI preparedness, Blockchain-powered transparency and Immersive Technology investment on sustainable business operations and economic change. Based on the World Development Indicators (WDI) data comprising measures of various countries and economic regions, proxy indicators of AI infrastructure, digital financial transparency, immersive digital innovation, ESG sustainability performance and economic growth were obtained and compared. The use of machine learning models such as Random Forest, Gradient Boosting and XGBoost was used to forecast outcomes in sustainability and economic transformation. The interpretability of the model was guaranteed with the help of SHAP-based feature importance analysis, whereas fairness analysis was done to identify ethical bias among income groups. The experiment shows that ethical infrastructure of digital type and blockchain enabled transparency can substantially enhance sustainability indicators of ESGs and growth outcomes of the economy. The investment on immersive technology also enhances sustainable economic efficiency. The results also indicate that predictive bias is measurable across income-level groupings and it is important to establish ethical governance in the assessment of sustainability based on AI. This paper can provide a solid empirical research that policymakers, ESG regulators and corporate leaders can use to embrace open, ethical and immersive digital policies as the means of achieving inclusive and sustainable economic change.

Effectiveness of Information Booklet on Knowledge Regarding Sequential Fetal Development among Antenatal Women

Komal Kadam, Nitanjali Patil — Sat, 10 Jan 2026 00:00:00 +0000

The study assessed the effectiveness of a structured information booklet on sequential fetal development among 150 antenatal women in a tertiary care hospital using one-group pre-test–post-test design. The questionnaire included items to assess the knowledge of antenatal women regarding sequential fetal development on a trimester-wise and month-wise basis. Knowledge was assessed before and after 7 days of intervention. Data analyzed using paired t- test and chi- square analysis. At pre-test, 70% of participants had average knowledge while 29% poor knowledge. Post intervention, 53% had average knowledge and elimination of poor knowledge. The mean score improved from 7.72 ± 2.04 to 13.34 ± 2.13 (t=23.303, p<0.0001). The study concluded that structured information booklet was highly effective and can be incorporated into routine antenatal education.

Effectiveness of a Video-Assisted Teaching Programme on Newborn Care Practices among Postnatal Mothers

Sat, 10 Jan 2026 00:00:00 +0000

The present study evaluated the effectiveness of a video-assisted teaching programme in improving maternal knowledge regarding newborn care. A one-group pre-test–post-test design was employed among mothers of newborns (n=60). Baseline knowledge was assessed using a validated questionnaire covering breastfeeding, hygiene practices, thermal care, identification of adequate feeding cues and recognition of common neonatal health problems. Participants then received a structured video-assisted teaching session for 2 days. Knowledge levels were reassessed on the seventh day. Pre- and post-test scores were compared to determine the impact of the intervention. After the teaching session, 63.3% of mothers demonstrated average knowledge, 26.7% exhibited good knowledge and 10% remained in the poor knowledge category. The increase in knowledge scores was statistically significant (t=10.73). The video-assisted teaching programme improved maternal knowledge of exclusive breastfeeding, proper positioning and attachment, hygienic practices, early identification of diaper rash and umbilical cord infection and signs of neonatal illness.

The Triple Threat in Practice: A Case Study of Stroke and Pulmonary Embolism in a Diabetic Patient

Aparna P. Patange, Pooja Manoj Jain, Pratik Pankaj Sarda — Sat, 10 Jan 2026 00:00:00 +0000

Diabetes mellitus is a well-established risk factor for both arterial and venous thromboembolic events due to its adverse effects on vascular function and blood clotting mechanisms. This case report presents a 54-year-old female diabetic patient who was admitted with severe hyperglycemia and diabetic ketoacidosis. Shortly after stabilization, the patient experienced sudden left sided hemiplegia, suggestive of an ischemic stroke. Despite prompt antiplatelet treatment and supportive care, patient had acute dyspnea and increased D-dimer levels resulting in a diagnosis of saddle pulmonary embolism. Prompt identification and treatment with anticoagulation (heparin shifted to rivaroxaban) let to marked improvements in both respiratory and neurological symptoms. This case highlights the “triple threat” and the complex pathophysiological interaction between diabetes, ischemic stroke and thromboembolic complications. The metabolic abnormalities associated with diabetes are likely contributed to a hypercoagulable state, which could further exacerbate the prothrombotic state.

Neutrosophic Uncertainty Modeling Along With Machine Learning For Breast Cancer Outcomes: A Hybrid Intelligent Framework for Medical Prognosis

Dr. S. Bharathi, Krithika. L — Mon, 09 Mar 2026 00:00:00 +0000

Strong prognostic techniques that can handle the inherent ambiguities, uncertainties, and inconsistent data common in clinical practice are essential for managing breast cancer. This paper introduces a novel hybrid computational framework that combines the predictive power of machine learning (ML) with the mathematical formalism of Neutrosophic Sets for uncertainty quantification. We propose that clinical data is not just imprecise but is essentially defined by three separate dimensions: falsity (contradictory evidence), indeterminacy (ambiguous or absent evidence), and truth (supportive evidence). In order to clearly characterize these aspects of uncertainty, our method first converts unprocessed clinical data into a neutrosophic feature space. The processed data is used to train and evaluate eight ML models (Support Vector Machine (SVM), k-Nearest Neighbors (K-NN), XGBoost, Logistic Regression, Random Forest, Neural Networks, Naïve Bayes, and Decision Trees) across five critical prognostic tasks: Diagnosis (Benign/Malignant), Recurrence, Chemotherapy Recommendation, Mortality and Survival. Several models achieved flawless performance (100% accuracy, precision, recall, and F1-score) in deterministic tasks such as Chemotherapy Recommendation and near-perfect diagnosis (SVM Accuracy: 97.37%, F1-Score: 97.93%), demonstrating the remarkable effectiveness of the framework. With the top F1-scores at 51.85% and 26.67%, respectively, the model outputs accurately reflect the inherent difficulties for challenging tasks like recurrence and survival prediction.

Energy-Aware Adaptive Edge Intelligence Model for Resource-Constrained IoT Networks

John Grasias S, K. Rajeswari, L. R. Sujithra — Tue, 10 Mar 2026 00:00:00 +0000

The rapid proliferation of Internet of Things (IoT) devices has intensified the demand for intelligent data processing at the network edge. However, resource-constrained IoT nodes suffer from limited battery capacity, restricted memory, low computational power, and dynamic workload variations, making the deployment of conventional deep learning models inefficient and energy-intensive. To address these challenges, this paper proposes an Energy-Aware Adaptive Edge Intelligence (EA-AEI) model designed specifically for resource-constrained IoT environments. The proposed framework integrates dynamic model scaling, energy-aware inference control, adaptive pruning and quantization, and context-driven task offloading to optimize computational efficiency while maintaining predictive accuracy. Unlike traditional static TinyML and fixed edge inference frameworks, the EA-AEI model continuously monitors residual energy levels and system load conditions to adaptively select the most suitable model configuration in real time. This adaptive mechanism significantly reduces energy consumption, minimizes latency, and prolongs network lifetime without compromising performance. Experimental validation conducted on representative IoT datasets demonstrates that the proposed model outperforms existing edge intelligence approaches in terms of average energy consumption, inference latency, throughput, and system sustainability. The results confirm that integrating adaptive intelligence with energy-aware decision-making enables scalable and efficient deployment of AI models in next-generation IoT networks.