Volume 13 | Issue 9 |

 Abstract

Orthognathic surgery has evolved from traditional two-dimensional (2D) planning methods to advanced digital approaches that integrate virtual surgical planning (VSP), computer-assisted navigation, and patient-specific implants (PSIs). VSP enables three-dimensional (3D) evaluation of dentofacial deformities, virtual osteotomies, and precise simulation of skeletal repositioning, thereby enhancing diagnostic accuracy and surgical predictability. Computer-assisted navigation provides real-time intraoperative spatial guidance, ensuring accurate translation of the virtual plan and minimizing risks such as nerve injury or malpositioning. Clinical studies demonstrate sub-millimetric accuracy in maxillary positioning, improved facial symmetry, reduced reoperation rates, and high patient satisfaction compared to conventional workflows. Although initial costs, training requirements, and integration of multimodal imaging remain challenges, these technologies improve efficiency, reduce intraoperative adjustments, and are particularly cost-effective in complex or high-volume cases. Emerging applications of augmented and virtual reality promise to further expand the role of digital navigation in orthognathic surgery.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Orthognathic surgery, Virtual surgical planning, Computer-assisted navigation, Accuracy, Oral and Maxillofacial Surgery

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The aim of this paper is to bring out the core concept of Bhakti in Dvaita Philosophy advocated by Sri Madhvacharya, which paves way as a means to Liberation. The purpose of this paper is to analyse and highlight the significance of Bhakti through which the Mumukshu can attain Salvation.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Bhakti, Isvara, Vairagya, Mahatmya, Upasana, Moksa, Jnana, Karma, Isvara-Prasada, Liberation, Samsara, mukta, Salvation.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A well designed and maintained water distribution network system is a cornerstone of modern society, underpinning public health, economic stability and quality of life. Its primary goal is to deliver a reliable supply of water with appropriate quality, quantity and pressure to satisfy the basically needs, etc. The study presents an in-depth analysis of pipe network modeling using EPANET for the District Metered Area (DMA) Gumadam, focusing on the optimization of hydraulic parameters and network performance evaluation. The primary objective was to assess the adequacy of the existing water supply infrastructure in terms of pressure distribution, flow velocities, and head losses at various junctions and pipe segments within the DMA. Comprehensive simulations were performed to evaluate the network, involving junction elevations, required demand, and pressure heads to ensure consistent delivery across all parts of the zone. Key findings indicated considerable variations in pressure and flow rates that were attributed to pipe diameters, material types (primarily HDPE of varying diameters), lengths, and elevations at junction nodes. Further, the study identified critical junctions and pipes prone to head losses, serving as focal points for future interventions to enhance hydraulic efficiency and operational sustainability. The research underscores the vital importance of DMA-based modeling for urban water distribution planning, facilitating targeted infrastructure improvements and energy savings. Through detailed tabulation and systematic analysis, the work provides practical recommendations for system upgrades and efficient resource allocation. Overall, the investigation demonstrates that EPANET-based modeling is a robust tool for optimizing water supply systems, ensuring reliable service and supporting long-term planning initiatives.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: EPANET, District Metered Area (DMA), hydraulic parameters, network performance evaluation, pressure distribution, flow velocities, head losses, resource allocation, pressure heads, HDPE, operational sustainability.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Early and accurate diagnosis of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) is vital for effective patient care and timely intervention. Magnetic Resonance Imaging (MRI) serves as a powerful modality for detecting structural brain changes, but traditional manual analysis is time-consuming and subjective. This study presents a novel hybrid framework that integrates the deep Convolutional Neural Network VGG16 with the local feature extraction capability of the Scale-Invariant Feature Transform (SIFT) algorithm to classify AD and MCI from MRI scans. The approach employs a feature fusion strategy that combines global high-level features from VGG16 with fine-grained local features from SIFT, eliminating the need for complex preprocessing steps like manual segmentation. Performance evaluation using confusion matrix-derived metrics demonstrates the framework's strong discriminative power. The model achieved an accuracy of 97.60%, sensitivity of 98.00% and specificity of 97.20%, highlighting its efficiency. These results confirm the model's high accuracy, robustness, and practicality, making it a promising tool for integration into clinical decision-support systems to facilitate early and reliable diagnosis of Alzheimer's Disease.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Learning, Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), Feature Fusion, Scale-Invariant Feature Transform (SIFT)

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This paper gives an overview about how the anomalies are detected in the network data containing the bytes sent and received and session duration while a bit of data is being transferred. The anomalies in the data play an important role in detecting security breaches, performance issues and abnormal behaviours while the network is being transmitted. The identification of anomalies face challenges, particularly when it comes in finding and managing false positives i.e., incorrectly flagged activities as abnormal or malicious. This paper aims in detecting all the anomalies in the given data and finding false positives in the data set. This paper gives different views about the different methods to detect anomalies in the given network data based on the false positive rates. We discuss different algorithms that are used in this detection including machine-learning algorithms. Here we also discuss the causes, and additionally we display the graph of the anomalies in the data. The analysis is used for improving the effectiveness of Intrusion Detection Systems for future.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

False Positives, True Negatives, Anomalies

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Utilizing pre-trained models allows the network to effectively recognize intricate patterns in medical images while minimizing training duration. The dataset is divided into four categories: normal, benign, malignant, and uncertain. Data preprocessing methods like image resizing and normalization were employed to enhance the model's performance and avoid overfitting. Moreover, data augmentation was applied to guarantee the model's ability to manage novel, unseen data effectively. The model's efficiency was demonstrated via visual representations of essential classification attributes and performance measures. The integration of CNNs and transfer learning presents a scalable and efficient approach for detecting lung cancer, delivering considerable benefits to healthcare practitioners who depend on AI-based diagnostic tools to improve patient results.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Lung Cancer Prediction, Convolutional Neural Networks (CNN), Transfer Learning, Deep Learning, Medical Image Classification, CT Scans, Artificial Intelligence in Healthcare

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The National Pension System (NPS) is an essential mechanism for safeguarding retirement security for Indian citizens, especially those in the unorganised sector. Since its establishment in 2004, the NPS has experienced substantial modifications, resulting in the launch of a new scheme with improved attributes. This study offers a comparative analysis of the previous and current NPS schemes, emphasising four critical variables: tax advantages, returns, risk-adjusted returns, and participation rates. The research employs a mixed-methods approach, integrating qualitative surveys and quantitative analysis to evaluate the financial advantages, risk profiles, and subscriber engagement of each scheme. The research indicates that the new NPS scheme provides enhanced financial returns, superior tax benefits, improved risk-adjusted returns, and increased participation rates relative to the previous scheme. The findings indicate that the new NPS scheme offers a more appealing risk-return profile and enhanced accessibility, leading to greater participation. Policymakers and financial institutions can utilise these insights to improve the scheme's efficacy. Future research may investigate the effects of global economic fluctuations on NPS returns and analyse the demographic variables affecting NPS participation.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

National Pension System (NPS), tax benefits, returns, risk-adjusted returns, participation rate, comparative analysis, new pension schemes, old pension scheme.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Healthcare datasets frequently exhibit severe class imbalance, where critical conditions represent minority classes, posing significant challenges for traditional machine learning approaches. This research investigates the development and implementation of interpretable ensemble methods using Python libraries to address class imbalance in healthcare applications while maintaining model transparency for clinical decision-making. Our proposed framework integrates advanced resampling techniques with ensemble learning algorithms, incorporating explainable AI components through SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to provide clinically meaningful insights. The methodology combines traditional ensemble approaches like Random Forest and Gradient Boosting with novel weighted voting mechanisms specifically designed for healthcare contexts. Through extensive experimentation on multiple healthcare datasets including diabetes prediction, heart disease diagnosis, and cancer detection, our framework demonstrates superior performance metrics . The implementation leverages core Python libraries including scikit-learn, pandas, numpy, and specialized packages like imbalanced-learn, making it accessible for healthcare data scientists. Results indicate that our interpretable ensemble approach not only addresses class imbalance effectively but also provides actionable insights that align with clinical knowledge, potentially reducing diagnostic errors and improving patient outcomes in real-world healthcare settings.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Class Imbalance, Ensemble Methods, Healthcare Analytics, Interpretable Machine Learning, SHAP, Python Libraries, Clinical Decision Support.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The modern job market is characterized by rapidly evolving skill requirements, making it challenging for students and professionals to identify and address the specific gaps in their knowledge for the desired career paths. This paper presents the Skill Gap Analyzer, a full-stack web application designed to provide a clear, data-driven, and personalized analysis of a user's skill set against industry requirements. The system compares user input skills against a comprehensive database of skills required for various job roles. The core features of the visualization of this "skill gap" through a series of interactive charts, which offers an intuitive understanding of the user's strengths and weaknesses. Furthermore, the application generates a dynamic learning path by fetching relevant high-quality educational resource from public APIs like YouTube for each identified missing skill. The project is implemented using a modern tech stack, featuring a React.js front-end, a Python FastAPI backend, and a MongoDB database, demonstrating a robust and scalable architecture.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Skill Gap, Career Development, Full-Stack, React, FastAPI, Data Visualization, REST API

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The banking environment is transforming from standard online banking to feature-rich mobile applications for fund transfers, bill payments, cross-border remittances, robo-advice, wealth management, etc. The allure of artificial intelligence-based financial innovations to reduce transaction costs has seen banking services undergo rapid transformation. Financial institutions are now deploying artificial intelligence (AI) and machine learning (ML) tools to meet growing customer demand for better, safer and more convenient ways to manage their money. The prime objective of this research is to analyse the customer perception towards AI enhanced features internet banking. The samples for this study were the general public from various districts in Tamil Nadu and it was selected using convenience sampling method. Mediation analysis is used to prove the two hypotheses. Direct Effect Hypothesis H1: AI-enhanced internet banking features will have a positive impact on customer perception and Mediation Hypothesis H2: The relationship between AI-enhanced internet banking features and customer perception will be mediated by Customer Experience. Exploratory factor analysis (EFA) using SPSS version 26, Confirmatory factor analysis (CFA), Structural Equation Modelling (SEM), Mediation analysis using AMOS 23 was undertaken to evaluate the proposed hypotheses

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Customer experience, Customer perception, Internet Banking.

  License

Creative Commons Attribution 4.0 and The Open Definition