Browse Items (11807 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. -
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. -
Pandemic Pulse: Unveiling Insights with the Global Health Tracker Through AI and ML
The current study highlights the importance of data analysis by applying data visualization tools to help you understand the pandemic disease informational component, and how it can be converted into knowledge that might enhance decision-making processes. In Tableau, a software for displaying data, researchers have incorporated a pandemic disease informational component from Coursera to improve assessment and selection. After becoming familiar with the data and the data visualization technological advances, some of it will be expected to conduct an initial investigation to identify significant changes in the data that is under consideration, compile and present this pandemic disease informational component, and enhance the corporate decision-making process. This issue for inquiry highlights the significance of knowledge examination via the use of communication visualization applications to aid in your comprehension of the pandemic disease informational component as well as how it may be changed into knowledge that may enhance the process of arriving at decisions. The creators of the knowledge representation computation application scenario used data from Coursera to improve their studies and make decisions. One will need to conduct an exploratory inquiry to find notable trends within the data after familiarizing oneself with it by utilizing visualization programs to compile and distribute this data to improve the company's decision-making procedures. This specific software is designed to be utilized in an early administrative duties course, an undergraduate accounting data structure course, or a data analytics-only educational program as a basic introduction to an informative visualization computer application. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A Comparative Analysis of LSB & DCT Based Steganographic Techniques: Confidentiality, Contemporary State, and Future Challenges
In order to maintain anonymity and security, the steganography is the technique of cloaking confidential data within what seems like harmless digital material. Several steganographic methods have been established devised over time, but those centered around the discrete cosine transformation (DCT) and the least significant bit (LSB) have drawn the most consideration. In this study, two common steganographic methods are compared and contrasted with an emphasis on the secrecy they can keep, the usage they are now receiving, and any potential difficulties in the future. As an alternative, the DCT-based method uses the frequency domain properties of cover media to obfuscate hidden information. Since it spreads the concealed information across several frequency coefficients, it provides greater security than LSB-based techniques. The resilience and imperceptibility of the concealed data are improved by a variety of DCT-based algorithms, such as the modified quantization and matrix encoding approaches, which we explore in detail. We also give a general summary of both approaches'current state in terms of their application, constraints, and areas in which they may be used. We evaluate the benefits and drawbacks of each approach, considering elements like payload size, computing difficulty, and detection resistance. 2023 IEEE. -
A Novel Framework for Harnessing AI for Evidence-Based Policymaking in E-Governance Using Smart Contracts
Harnessing AI for evidence-based policymaking in e-governance has the potential to revolutionize the way governments formulate and implement policies. By leveraging AI technologies, governments can analyze vast amounts of data, extract valuable insights, and make informed decisions based on evidence. This chapter explores the various ways in which AI can be employed in e-governance to facilitate evidence-based policymaking. It discusses the use of AI algorithms for data analysis and prediction, enabling governments to identify patterns, trends, and emerging issues from diverse data sources. Moreover, AI-powered tools can enhance citizen engagement and participation, by facilitating data-driven decision-making processes and providing personalized services. Additionally, AI can assist in policy evaluation and impact assessment, by automating the collection and analysis of data, thus enabling governments to measure the effectiveness of their policies in real-time. Furthermore, AI can contribute to enhancing transparency and accountability in e-governance, by automating processes such as fraud detection and risk assessment. Despite the immense potential, the adoption of AI in e-governance must address challenges such as data privacy, algorithmic bias, and ethical considerations. This chapter concludes by emphasizing the importance of building trust, ensuring fairness, and promoting responsible AI practices to maximize the benefits of AI in evidence-based policymaking for e-governance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
Synergizing Senses: Advancing Multimodal Emotion Recognition in Human-Computer Interaction with MFF-CNN
Optimizing the authenticity and efficacy of interactions between humans and computers is largely dependent on emotion detection. The MFF-CNN framework is used in this work to present a unique method for multidimensional emotion identification. The MFF-CNN model is a combination of approaches that combines convolutional neural networks and multimodal fusion. It is intended to efficiently collect and integrate data from several modalities, including spoken words and human facial expressions. The first step in the suggested system's implementation is gathering a multimodal dataset with emotional labels added to it. The MFF-CNN receives input features in the form of retrieved facial landmarks and voice signal spectroscopy reconstructions. Convolutional layers are used by the model to understand hierarchies spatial and temporal structures, which improves its capacity to recognize complex emotional signals. Our experimental assessment shows that the MFF-CNN outperforms conventional unimodal emotion recognition algorithms. Improved preciseness, reliability, and adaptability across a range of emotional states are the outcomes of fusing the linguistic and face senses. Additionally, visualization methods improve the interpretability of the model and offer insights into the learnt representations. By providing a practical and understandable method for multimodal emotion identification, this study advances the field of human-computer interaction. The MFF-CNN architecture opens the door to more organic and psychologically understanding human-computer interactions by showcasing its possibilities for practical applications. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
An IoHT System Utilizing Smart Contracts for Machine Learning -Based Authentication
The Internet of Healthcare Things (IoHT) and blockchain technologies have made it feasible to share data in a secure and effective manner, but it is still challenging to ensure the data's veracity and privacy. This paper presents a blockchain authentication method that utilizes Machine Learning (ML) techniques that use smart contracts to ensure the security and privacy of IoHT data. The process utilizes smart contracts to manage access control and ensure data integrity, and deep learning algorithms to identify and validate the accuracy of user data. Furthermore, the approach improves the resilience and dependability of the authentication process and permits secure data ex-change between multiple IoHT systems. The proposed approach provides a potentially revolutionary solution to enhance the safety and confidentiality of IoHT data. It has the potential to fundamentally change how healthcare is provided in the future. 2023 IEEE. -
Progressive loss-aware fine-tuning stepwise learning with GAN augmentation for rice plant disease detection
Modern technology like Artificial Intelligence (AI) must be used in the agricultural sectorif sustainable agricultural output is to be achieved. One of the most convenient strategies for resolving current and future issues is data-driven agriculture. For this, disease prediction is a major task for precise farming. For predictive analysis and precise agriculture monitoring systems, with the application of AI, Machine Learning (ML) and Deep Learning (DL) play vital roles in building a more robust system. In this work, we will design a DL-integrated rice disease prediction system to be implemented for precise farming. Improvisation of the developed model to detect rice plant diseases & pest attacks with a high level of precision. In this work, the Progressive Loss-Aware Fine-Tuning Stepwise Learning (PLAFTSL) model is proposed for disease detection. For step-wise learning fine-tuned ResNet50 model is used with the introduction of freezing and unfreezing layers. This reduces the training parameters and thus computational complexity. The introduction of the step-wise and progressive loss-aware layer will result in fast convergence and improved training efficiency during information exchange among layers respectively. Our proposed work uses a dataset from two sources. The result analysis is presented with an ablation study. Additionally, the baseline model, ResNet50, is used to display the outcomes of the ablation. The results demonstrate that the fine-tuned model results in better performance as compared to the transfer learning model. The Conditional Generative Adversarial Network (cGAN) augmentation is also added to the designed model which will improve detection effectiveness and can also manage the imbalance in input data. The model has achieved approx. 98% accuracy and outperforms better with comparative state-of-art models. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Discovering patterns of live birth occurrence before in vitro fertilisation treatment using association rule mining
According to estimates, in-vitro fertilisation (IVF) is credited for the delivery of over 9 million children globally, constituting it to be a highly remarkable as well as commercialised advanced healthcare treatment. Nonetheless, the majority of IVF treatments are now constrained by factors such as expense, access and most notably, labour-intensive, technically demanding processes carried out by qualified professionals. Advancement is thus crucial to maintaining the IVF markets rapid growth while also streamlining current procedures. This might also improve access, cost, and effectiveness while also managing therapeutic time efficiently and at a reasonable cost. IVF has become a renowned technique for addressing problems like endometriosis, poor embryo development, hereditary diseases of the parents, issues with the biological function, problems with counteracting agents that harm either eggs or sperm, the limited capacity of semen to penetrate cervical bodily fluid, and lower sperm count that lead to infertility in humans. Copyright 2023 Inderscience Enterprises Ltd. -
Analysis of Fraud Prediction and Detection Through Machine Learning
In today's world the rate of fraudulent activities has significantly elevated, because of which a need for a competent system is required. Among all the fraudulent activities insurance fraud has the most dominating rate of growth. Fraud studies have suggested, that upon identifying the similar characteristics of a fraudulent claim with the claimants, a system of forensic and data-mining technologies for fraud detection can be set up. In this, seek to define fraud and fraudster, and look at the types of fraud and followed by the consequences of fraud to financial systems. As fraud is getting widespread these days epically in the health care insurance system, dealing with this problem has become a necessity. Unsupervised machine learning algorithms such as K-Means clustering along with supervised algorithms used in machine learning, like support vector machines, logistic regression, design trees etc. can play a very vital role in binary class classifications, which would ultimately help in identifying and outreaching the desired goal of fraudulent detection. In the end, this paper specifies the best or the most appropriate model that could be used in the given dataset to produce the most accurate results, based on certain parameters of confusion metrics like accuracy, precision, and specificity. 2023 IEEE. -
Blockchain Computing: Unveiling the Benefits, Overcoming Difficulties, and Exploring Applications in Decentralized Ledger Infrastructure
The protocol known as blockchain, which is composed of blocks, utilizes a decentralized distributed system of nodes (miners). There are three parts to every block: information, which is represented by a hash, and the hash of a previous transaction. In order to regulate data after it has been stored, it is quite difficult to make changes. Mining is compensated for each encrypted function computation they carry out to verify the transaction. This research paper will provide a comprehensive understanding of blockchain-based technologies and how they are applied in a variety of industries, including those that deal with digital currencies, financial services, medical manufacturing, privacy, and a number of other fields. Digital money, notably the cryptocurrency Bitcoin, had previously been one of the most well-known network applications. As there have lately been several studies about the unique utilization of this sort of technology, we will discuss some of these academic works as well as the challenges encountered during the development of these kinds of applications. Blockchain technology is a quickly growing area of database technology that has recently found use in a wide range of industries, including the use of digital money, hospital administration, and other academic subjects. Because of how blockchain technology works and operates, these types of applications are now possible. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A multi-model unified disease diagnosis framework for cyber healthcare using IoMT-cloud computing networks
The past several decades of research into machine learning have been of great assistance to humanity in the diagnosis of a variety of ailments using various forms of automated diagnostic procedures. Machine learning, combined with smart health devices, has improved health monitoring, timely diagnoses, and treatment. This paper introduces a unified disease diagnosis framework, integrating cloud computing, machine learning, and IoT. The framework has three layers: physical (collects patient data), fog (intermediate layer with a domain identification unit to determine input and diagnosis type), and transmission (cloud server with a disease detection unit). The performance evaluation shows the robustness and efficiency of the model as compared to state-of-art models. 2023, Taru Publications. All rights reserved. -
Combatting Phishing Threats: An NLP-Based Programming Approach for Detection of Malicious Emails and Texts
Attackers are employing more advanced strategies to trick people into divulging private information or carrying out harmful deeds, and phishing is still a serious cybersecurity risk. We provide a new method in this study that combines algorithms based on AI-based expert systems and deep learning (ML) with the use of NLP-based programming (NLP) approaches to identify fraudulent emails and messages. Using a variety of datasets that include samples of both authentic and phishing messages, our approach preprocesses textual data, extracts relevant characteristics, and trains AI-based expert systems and deep learning models. Metrics including accuracy, precision, recall, and F1-score are used to assess the effectiveness of different AI-based expert systems and deep learning methods, such as logistic regression, random forests, decision trees, and neural networks, among others. To collect semantic information and increase detection accuracy, we also investigate the integration of sophisticated NLP-based techniques, such as word embeddings. The efficacy of our suggested strategy in reducing this common cybersecurity issue is highlighted by our results, which show promising performance in correctly recognizing phishing attempts. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.