Browse Items (16481 total)
Sort by:
-
Impact on block chain technology in public sector of India /
Patent Number: 202241047512, Applicant: M Mohamed Fazil.
Commercial and non-profit use of the blockchain technology across the world has demonstrated its significant advantages over traditional arrangements. The technology appears to be the most appropriate in areas that require storage and processing of large amounts of protected data. Effective exploration and experimentation with the technology in a variety of fields depends on a favorable legal environment. -
Secure image retrieval and classification framework for IOT based healthcares systems using deep neural networks /
Patent Number: 202241035066, Applicant: Dr.S.Balamurugan.
Deep Learning has shown promising results in the domain of Medical Image Analysis and Image Processing. Proposed is a secure image retrieval and classification framework for IoT based healthcare systems using Deep Neural Networks. The problem of solving the error introduced by adversarial noise is considered. Back Propagation Algorithm is employed for Segmentation (localization) as well as error prediction and detection. -
Unstructured data extraction system using multi head attention and a novel language model /
Patent Number: 202141056398, Applicant: K. P. Kavitha.
A system 100 for Offline handwritten text recognition (HTR) of a scanned handwritten text input image leveraging Modern Deep Recurrent Neural Network (RNN). System 100 comprises (RNN) is proposed with the help of the present's embodiments disclosure (RNN). A cursive eliminated handwritten text image is mapped to a multi-head attention-based sequence-to-sequence learning applying the beam search technique and employing an RNN-based variable-length encoder-decoder architecture. -
An Expert System for Diabetes Diagnosis
Expert system is a computer system that emulates the decision making ability of a human expert. That is it acts in all respects like a human expert. It uses human knowledge to solve problems that would require human intelligence. The expert system represents expertise knowledge as data or rules within the computer. These rules and data can be called upon when needed to solve problems. Diabetes is a knotty disease and very common in the modern world. Diabetes is a serious disease that affects almost every organ in the body like heart, eyes, kidney, skin, nerves, blood vassals, foot etc. If left the disease unchecked it will make serious complications including death. Though the disease can not possible to cure completely, it can be well managed or control and can lead a very healthy life. Early diabetes diagnosis plays a crucial role in diabetic control, and can prevent further medical complications. This paper presents the design and development of medical expert system for Diabetes disease and it support diagnosis, give information about complications and act as diabetes trainer. It used rule based approach to collect data and forward chaining inference technique. This system provides a user interactive, menu driven environment. Symptoms and risk factors associated with diabetes are taken as the basis of this study. In case of diagnosis the system will ask a bunch of questions about the symptoms and risk factors to the expert system user and user should give yes or no answer. According to the answer the system will make judgment about the possibility of illness, how much severe it is like slight chance, moderate chance, high chance, very high chance, diabetic or not. If the user wants to know the details of diabetes complications he can select the complication option from the menu. It can also used in teaching practice. The system is drawn up with CLIPS expert system building tool version 6.3 and in Windows/Dos environment. -
Deep Learning-Based Health Risk Prediction in Contact Sports Using Wearable Sensor Data
This study presents a deep learning-based approach to predicting physiological health risks in athletes engaged in contact sports using wearable sensor data. Motivated by the need to detect early warning signs of collapse or severe fatigue, this study employs a Long Short-Term Memory (LSTM) neural network to analyse multivariate time-series data. Key physiological signals, including heart rate, body temperature, and motion, were extracted from the PAMAP2 dataset to train and validate the model. The LSTM demonstrated strong predictive performance, achieving an accuracy of 98.3% in identifying potentially dangerous physiological states. In addition to its high classification accuracy, the model effectively captured temporal dependencies in the data, underscoring its suitability for health risk prediction in dynamic, high-intensity sports environments. This study highlights the potential of wearable data and LSTMbased analysis in supporting proactive athlete health management and injury prevention. 2025 IEEE. -
Alexithymia and Internet Addiction: Mediating Role of Social Connectedness, Impulsivity, and Moderation by Depression
Internet addiction is a mounting concern in current times. Recent studies indicate a link between alexithymia and Internet addiction, but the underlying mechanisms of this association require more investigation. The present study explores the relationship between alexithymia and Internet addiction, with the mediating effect of Impulsivity and social connectedness, and the moderating effect of depression. A convenience sample of 362 participants between the ages of 18 and 25 years participated in this study and completed the Youngs Internet Addiction Test, Toronto-Alexithymia Scale, The Social Connectedness Scale, Barratt Impulsiveness Scale 15, and The Centre for Epidemiological Studies Depression Scale Revised. The results indicate that the direct effect of alexithymia on Internet addiction is partially mediated through impulsivity and social connectedness. Further, the moderating effect of depression is found to be non-significant. The results revealed two possible pathways through which alexithymia influences Internet addiction. Future research and interventions on Internet addiction can use these findings to mitigate the adverse outcomes of Internet addiction. 2025, PsychOpen. All rights reserved. -
Digital Twinning of an Electric Two-Wheeler for Real-Time Battery State Estimation and Augmented Reality Visualization
This paper describes the design and structure of a digital twin system for an electric two-wheeler. It enables real-time battery state estimation and augmented reality visualization. Using digital twin technology, the system improves battery monitoring, rider experience, and operational efficiency through real-time simulation and optimization. The structure includes a physical sensing layer, an ESP32-based processing unit, cloud-based data management using ThingSpeak, and an AR front-end. It tackles key challenges in estimating the state of LiFePO4 batteries, such as voltage plateau behavior, temperature sensitivity, and accuracy of algorithms. The system also considers the limits of microcontroller-based sensing and data interfacing, ensuring reliable performance in real-time. 2025 IEEE. -
A Study on the impact of foreign investment in infrastructure sector in india
The growth of an economy is determined by the amount of investment made or the capital created in the economy. Capital creation happens when the economy has excess of income over expenditure, in other words, savings. newlineForeign Investment is a good source of fund for developing economies whose savings is low. Hence, opening up the economy for inflow of foreign funds has almost become inevitable in the present situation of liberalization, privatization and globalization. Therefore, all developing economies, including India, are creating opportunities for foreign investments. Infrastructure plays a pivotal role in development of a country. However infrastructure projects require huge investment and the projects take a long time for the projects to be completed. This necessitates investment inflows to originate from the Government, PPP, FDI, etc. Foreign investment through foreign direct investment and Foreign Institutional Investment has newlinebecome a popular source of investment, particularly for financing the projects newlineof infrastructure sector. Foreign funds flow into the firms through investment in the equity of the firms Infrastructure plays a pivotal role in development of a country. newlineHowever infrastructure projects require huge investment and the projects take a long time for the projects to be completed. This necessitates investment inflows to originate from the Government, PPP, FDI, etc. Foreign investment through foreign direct investment and Foreign Institutional Investment has newlinebecome a popular source of investment, particularly for financing the projects newlineof infrastructure sector. Foreign funds flow into the firms through investment in the equiy of the firms Regression analysis is used to ascertain the functional relationship among FDI, Growth, Trade enness, Economic Stability and Energy position. The result of the regression analysis proves that there exists a functional relationship between FDI equity inflows and growth and trade openness. -
Culinary influence on Bengaluru as a tourism destination /
International Journal of Recent Technology And Engineering, Vol.8, Issue 4, pp.766-774, ISSN No: 2277-3878. -
A study on Challenges of Indian Hospitality Industry and Remedies For Sustainability in the Ever Changing Market Scenario.
VOLUME NO. 3 (2013), ISSUE NO. 11 (NOVEMBER) ISSN: 231-1009 -
Wall jet nanofluid flow with thermal energy and radiation in the presence of power-law
The effectiveness of jet flow in the energy transfer process has made it very useful in industrial applications. These flows also have higher heat transfer coefficients than traditional cooling through convection. The appliances inclusive of the jet make effective use of fluid and enhance the heat transfer rate. The contemporary article investigates the jet flow of power-law nanofluid past a moving wall. The nanofluid is formed by suspending Cu and Al2O3 nanoparticles in water. Furthermore, the jet flow is analyzed in the presence of radiation, which is further assumed to be linear, and the application of Rosseland approximation is considered to be valid. Considering these aspects, the model is designed using partial differential equations (PDE), which are then converted to a system of non-linear ordinary differential equations (ODE) by implementing certain similarity transformations. Thus, the obtained system is solved using numerical methods, and the results are discussed with the help of graphs. The significant conclusions of the analysis were that the increase in the radiation parameter contributed to the increase in the temperature of the nanofluid. The increase in the Prandtl number reported a decrease in the amount of heat absorbed by the nanofluid. 2023 Taylor & Francis Group, LLC. -
Effect of glass and coir fiber on geotechnical properties of clayey soil
The use of fibers for the improvement of weak subgrade soils is beneficial as it not only acts as reinforcement but also, increases drainage, provides better workability, inexpensive and required in exiguous quantity. Available studies on clay soil reinforced are limited to a particular type of fiber, any comparative study on two or more types of fibers on same soil, provides a useful information on understanding suitability of specific type of fiber. This study deals with experimental characterization of clay soil reinforced with glass and coir fibers. California Bearing Ratio (CBR) and Unconfined Compressive Strength (UCS) tests were performed on these fiber reinforced clay samples with different percentage of glass and coir fibers. The results of these unreinforced and reinforced soils are compared. 2019 SERSC. -
A physics-informed neural network framework for consolidation parameter prediction using controlled clay-sand mixtures
This paper introduces a novel Physics-Informed Neural Network (PINN) model for predicting the coefficient of consolidation (Cv) in high plasticity clays. The model was trained from experimental data obtained from controlled clay-sand mixtures. The input parameters include clay content, Atterberg limits, initial void ratio, compaction energy, applied pressure and consolidation characteristics like compression index (Cc) and volumetric compressibility (mv). Additional parameters like plasticity index, porosity, activity-clay interaction and compaction efficiency were derived from feature engineering. The proposed PINN model combines data-driven loss and physics-based loss into a total loss function. The physics loss includes three constraints derived from modified Kozeny-Carman equations, activity-based mineralogical relations, and compression-volume consistency. Hyperparameter optimization identified the optimal configuration: 800 epochs, learning rate 0.001, architecture [128, 64, 32], and physics loss weights distributed as 0.7, 0.25, and 0.05. Five-fold cross-validation demonstrated robust performance (R2 = 0.9903 0.0026), significantly outperforming baseline neural networks (R2 = 0.9682 0.0126, p = 0.0116) with 73.9% reduction in Root Mean Square Error (RMSE = 6.37 10-11 m/s) and 5.71% improvement in Mean Absolute Percentage Error (MAPE = 4.48%). External validation showed the PINN (R = 0.9968) substantially outperformed empirical correlations (best R2 = 0.1636) and conventional machine learning models (best R2 = 0.9878). SHapley Additive exPlanations (SHAP) interpretability analysis validated physically meaningful decision-making, with plastic limit and activity emerging as primary drivers. This framework provides a transferable, physics-consistent solution applicable across diverse clay types for foundation design and site characterization. Copyright 2026. Published by Elsevier B.V. -
Decentralized Data Integrity: Integrating MySQL with Blockchain for Resilient Healthcare Systems
A transformational solution to the problems created by healthcare data management is presented by the integration of MySQL and blockchain technology, centered around security, scalability, and efficiency. This paper presents MBHA MySQL-Blockchain Healthcare Architecture combining thestructured data storage, querying capabilities of MySQL with the decentralized, tamper-proof framework of blockchain. The system shows impressive performance metrics with an average API response time of 1.54 seconds for user registration and 841 milliseconds for login. The database queries and data retrieval or insertion took less than 1 millisecond, and JWT tokens were generated for authentication in less than 50 milliseconds. Conclusion Results indicate an efficient real-time system to accomplish tasks with integrity in terms of data but also with safety in operations. This architectural model, discussed above, is issues regarding data security and access with a need to provide care-collaboration needs. Scalability would then be optimized while keeping down computational overhead; in fact, work toward readiness for adoption is mainly towards being more regulatory compliant. 2025 River Publishers -
IT Strategies for Effective Marketing in Globally Diverse Corporate Environments
Today, organizations discern multicultural teams, dynamic consumer tastes and shrinking landscapes of competition bordering on the internet-centric global economy. This paper investigates the role of IT strategies in improving the strategic marketing within different corporate environments. An examination of IT's role in addressing marketing and management complexity in different cultural context is made. Theoretical models are reviewed and responsible global marketing practices are promoted through digital transformation to reshapes the business operations. We also illustrate IT based solutions for dealing with cross cultural communication barriers, resistance to change as well as team dynamics. In this case study and trend analysis with trends, we show how own best practices of market segments, digitalization as well as cross cultural management work together with IT to encourage agility, customer focus, and continuous learning in the organization. 2025 IEEE. -
Enhanced Pneumonia Detection from Chest X-rays Using Machine Learning and Deep Neural Architectures
Pneumonia is a major worldwide health concern, particularly for vulnerable groups such as babies and the elderly. Despite advances in medical imaging, diagnosing pneumonia using a chest X-ray remains difficult, due to the subtle presentation of symptoms and the variety in picture interpretation. This study utilizes modern machine learning can improve the accuracy and speed of diagnosing pneumonia using chest X-ray images. Utilizing a comprehensive dataset from the Kaggle online repository, consisting of over 5,000 annotated images, we evaluate the efficacy of various machine learning models including deep convolutional neural networks (CNN) and ensemble learning techniques. Our findings indicate that models like the Fuzzy opponent histogram filter combined with Logistic model trees (LMT) achieved the highest accuracy at 96.97%, while the deep learning-based Lenet (CNN) with LMT closely followed at 95.85%. The study aims to improve diagnostic precision, reduce interpretation discrepancies, and facilitate faster clinical decision-making by identifying the most effective machine learning approaches for real-world applications in healthcare settings. 2025 Kamal Upreti, Anju Singh, Divakar Singh, Preety Shoran, Uma Shankar, Meenakshi Yadav and Rituraj Jain. -
AQUAPHISH: Leveraging Metaheuristics and Automated Machine Learning for Precision Phishing Detection
Phishing is an ongoing and dynamic threat in the field of cybersecurity, targeting user trust to capture sensitive data through fraudulent websites. Conventional detection systems tend to use binary classification and static features, which make them less flexible to new attack paradigms. This paper seeks to design a solid and comprehensible phishing detection system that alleviates the drawbacks of binary labeling by proposing a regression-based risk scoring model. The aim is to improve accuracy, feature interpretability, and deployment in real-time settings. The new method combines Whale Optimization Algorithm (WOA) for feature selection and H2O AutoML for model creation and assessment. A filtered dataset of 10,000 phishing and normal websites is operated upon using 48 features, which are then reduced to 36 using WOA. The last models are optimized with H2O AutoML, encompassing ensemble learners, and tested on various regression metrics. Interpretability is achieved with SHAP analysis. The best model had an R of 0.9534, RMSE of 0.1079, and MSE of 0.0116, better than traditional classification-based phishing detectors. The system, with only 36 features, had training time decreased by 23.6% and inference latency reduced by ~18%, without any sacrifice in detection accuracy (98.3%). Regression-based scoring also supported adaptive threat ranking in real time. By posing phishing detection as a regression problem and integrating metaheuristic feature selection with AutoML, this work introduces a scalable and explainable framework ready for real-world deployment. The low-latency yet high-accuracy model is best suited for integration into browser-level phishing filters and cloud-based threat intelligence platforms. 2025, Interdisciplinary Publishing Academia. All rights reserved. -
Enhancing Diagnostic Precision in Lung Cancer Detection Using Smote-Based Balancing Techniques
Worldwide, Lung cancer is the primary cause of death from cancer, and chances of surviving are considerably raised by early detection. While traditional diagnostic approaches heavily rely on imaging and specialized infrastructure, they often fail to serve low-resource or early screening environments. In this work, based on deep learning, lightweight framework for detecting lung cancer from structured survey data is presented. The research tackles the prevalent the problem of class disparity using the Synthetic Minority Over-sampling Technique (SMOTE), enhancing the sensitivity of predictive models. A comparative evaluation was conducted across six models Logistic Regression, SVM, KNN, Naive Bayes, Random Forest, and XGBoost. Among these, Random Forest and XGBoost achieved 95% accuracy, 0.98 recall, and ROC-AUC scores of 0.9943 and 0.9835 respectively. The proposed hybrid ensemble model (Random Forest + XGBoost) outperformed all with 96% accuracy, 0.95 precision, 0.98 recall, and a ROC-AUC score of 0.9961. These findings demonstrate that the hybrid strategy is effective in providing high diagnostic precision using clinical survey data that is not imaging. 2025 IEEE. -
Integrated biogasification and carbon capture pathways: a system-level review of technologies, storage options, and deployment challenges
Carbon-negative energy systems that integrate bioenergy production with permanent carbon dioxide (CO2) sequestration are increasingly recognized as essential for achieving global net-zero and beyond-zero climate targets. While extensive research exists on individual components such as biogasification, carbon capture technologies, and geological storage, a coherent system-level synthesis linking these pathways remains fragmented. This review addresses this gap by providing an integrated assessment of biogasification-based carbon capture and storage (CCS) systems, with particular emphasis on techno-economic performance, capture efficiency, subsurface storage options, and deployment challenges. Following the PRISMA 2020 guidelines, 112 studies were systematically selected from an initial pool of 780 publications and analyzed to compare advanced gasification routes, emerging capture technologies, and storage strategies. The results indicate that hybrid gasificationsolid oxide fuel cell systems can achieve efficiencies of up to 55%, while cryogenic carbon capture consistently delivers CO? purities above 95% with reduced energy penalties. Supercritical water gasification and hydrothermal pathways demonstrate strong potential for wet biomass conversion, achieving hydrogen yields exceeding 1150 mmol/L and carbon efficiencies above 80%. Despite these technical advances, large-scale deployment remains constrained by high costs (USD 8001350 per tonne CO2), infrastructure limitations, and policy uncertainty. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026. -
A Comprehensive Study Using Convolutional Neural Networks as a Method for Multi-class Skin Cancer Image Classification
Skin disorders occur more frequently than other kinds of diseases. Skin diseases can be attributed to a number of aspects, like fungi, bacteria, viruses, allergies, and so on. The rapid advancement of healthcare centered around lasers and photonics has rendered it feasible to diagnose skin disorders in a more accurate and timely manner. However, the cost of such a diagnostic remains extremely limited and prohibitively expensive. As a result, the use of image processing methods is beneficial in the initial phases of designing a computerized dermatology screening system. The retrieval of characteristics is an extremely important step in classifying skin disorders. The use of computer vision may play a crucial role in the diagnosis of a variety of skin conditions using a variety of approaches. The strategy we have proposed is straightforward and quick and requires no expensive technology besides a computer and a camera. When applied to the inputs of a colored picture, the method is successful. After that, resize a portion of the image to retrieve attributes with a pretrained convolutional neural network. The attribute was then classified using the multi-class XGBoost program. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.