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Blockchain Based Security System for Health Care 2

Blockchain Based Security System for Health Care

引用

2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Ankit Singh, Rishipal Guru Jambheshwar University of Science & Technology Dept. of Computer Science & Engineering Hisar India

ISBN: (纸本)9798350383652

Security in healthcare systems based on blockchain technology is the subject of ongoing research. Block Chain technology has the potential to revolutionize many different sectors since it provides a decentralized, incontestable, and publicly verifiable record of every transaction activity. Bitcoin's foundational mechanism is referred to as 'block chain technology.' This technology allows for completely decentralized transaction processing. Healthcare aims to diagnose, treat, relieve, and cure any and all bodily and mental health issues, as well as to promote health and prevent sickness. Since all transactions in a blockchain are encrypted, it provides the highest level of security possible. Blockchain networks provide the same assurance of security for medical records. The integrity of the network's ledgers may be checked by checking file signatures on each node to make sure no alterations have been performed. The signature will no longer be valid when the record has been modified. The future of blockchain and big data applications is promising. Big Data technology can manage any kind, amount, or speed of data, while blockchain enhances efficiency across all industries. It has been noticed that medical records are maintained on a blockchain for reasons of security. Proposed system has evaluated healthcare data for security. When compared to the results of cutting-edge research, however, the scalability and adaptability of traditional methods are clearly lacking. Better performance and security utilizing blockchain technology is essential, hence developing such a system is a priority. © 2024 IEEE.

关键词： Electronic health record

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VoteChain: Promising a Secure and Transparent Election using Blockchain and Biometrics 4

VoteChain: Promising a Secure and Transparent Election using...

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Vivekanandan, G. Prabahar Godwin James, T. Divya, B. Tejas, Madhav V. Naveen Karthick, T.K. Yuvanesh, P. Sri Sai Ram Institute of Technology Department of Computer Science and Engineering India Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India

ISBN: (纸本)9798331508845

Democratic voting is one of the significant aspects of governance that has various complications which are as follows;Complication arising from transparency Complication arising from low voter turnout Complication arising from vulnerability to the manipulation of the elections Complication arising from low confidence in the election process Complication arising from forgery of voter id cards Complication arising from delays in the announcement of the results Complication arising from security breach. All these issues erode the credibility of elections and public faith. To them, blockchain has a perfect solution in that it is decentralized, secure, transparent, and cannot be tampered with technology. To realize our e-voting system, we built an evoting application as a smart contract on the Ethereum platform using digital wallets and solidity. A given voter is assigned tokens (gas) which are utilized every time they cast their vote in order to eliminate vote forgeries and other undesirable incidents. This blockchain-based system does not require physical polling stations;also, voting is scalable and secure with flexible consensus algorithms, cryptographic hashing, and measures in response to a 51% attack. It also incorporates fingerprint scanning, to further discourage fraud and make sure that the process is completely secure and transparent. Since these records are stored in a blockchain, the ability of a single unscrupulous agency trying to manipulate the records is very hard given that records are validated by the nodes in the network, making the voting process more secure and credible. © 2024 IEEE.

关键词： Blockchain

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Diffusion Models for Automatic Music Mixing

Diffusion Models for Automatic Music Mixing

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Wu, Xinyang Horner, Andrew Hong Kong University of Science and Technology Dept. Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350362480

Music mixing is a process that involves fine-tuning the levels, dynamics, and frequency content of musical elements to ensure clarity and harmony in the final music production. In this paper, we present an automatic mixing system based on diffusion models to correct imbalances in music mixes. We manipulate the well mixed stems' short-time Fourier transform randomly to simulate the frequency and level imbalances commonly encountered in real-world scenarios. The difference between the imbalanced mix and the original mix is treated as noise for the diffusion model to predict, enabling the reverse denoising process to generate an automated mix. We evaluate our model's performance by calculating the signal-to-distortion ratio between the original and predicted mixes. These results are compared with baseline automatic mixing models, demonstrating significant improvements. Test set results in audio: https://***/diffmusicmix/ © 2024 IEEE.

关键词： Signal denoising

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An Effective Anti-Money Laundering System Using Block Chain technology

An Effective Anti-Money Laundering System Using Block Chain ...

引用

2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Divya, B. Thara, P. Kavitha, S. Alvin Ancy, A. Preethi Sri, R. Dhanalakshmi, D. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Dept of Computer Science and Engineering India India Chennai Institute of Technology Dept of Electronics and Communication Engineering India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378177

In the developed financial services industry, identity is defined using government-issued identification cards. Nevertheless, the primary obstacles in the consolidation and authentication of these data in connection with large-scale anti-money laundering (AML) requirements have significantly increased the challenges for individuals seeking to enter the financial system. This renders it exceedingly unprofitable and a greater risk for banking systems to engage with developing economies that might lack even the most primitive forms of identification. Furthermore, the process of establishing a banking account is packed with difficulty as a result of protracted processing times, substantial report obligations, and ambiguity surrounding the utilization of confidential customer data. The objective of cash laundering is to return illegal funds to their source;therefore, money launderers typically employ robust financial structures to transport funds. Additionally, the level of money laundering activity may become more geographically concentrated as the amount of money laundered increases. For many years, the financial quarter has been working to reevaluate the concept of identity. Blockchain is an emerging computing structure that enables the integration of disparate data points from authoritative source vendors into a unified digital ledger of validated, unalterable, transparent, and cryptographically protected data. Furthermore, secrecy can be ensured by letting the appropriate person or group control how their identity is shared. A blockchain is a distributed, virtual ledger that is unchangeable and keeps track of transactions in chronological order and close to real time. In order for later transactions to be added to the ledger the community members also known also nodes, must agree on them. This establishes an ongoing management mechanism that oversees issues related to manipulation, errors, and statistics. After dispatch Information contained within a block cannot be

关键词： Laundering

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Impact of Machine Learning in Music Genre Classification using CNN 14

Impact of Machine Learning in Music Genre Classification usi...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Venkatesh, Jorige Kannan, Karthik Ayyadurai, M. Sathish, M.G. SRM Institute of Science and Technology Dept. of Computer Science and Engineering Chennai Ramapuram India

ISBN: (纸本)9798350335095

The rapid expansion of available music tracks has made the classification of music genres increasingly crucial. To achieve accurate genre predictions, machine learning techniques have been widely employed and proven highly efficient. In this study, we focused on enhancing the accuracy of music genre classification systems by training a Convolutional Neural Network (CNN) model using Keras. Music genre classification serves as a valuable tool for music learning, offering a reliable method of categorization. Given the immense amount of data and the inherent complexity in identifying music genres, we leveraged the power of deep neural networks to classify ten distinct genres. To evaluate the effectiveness of our approach, we compared the performance of the CNN model to other existing models using the GITZAN dataset. Our results demonstrated the success of the CNN model in accurately classifying music genres, surpassing previous studies while requiring less data. This research contributes to the field of music genre classification by providing an advanced and efficient solution using deep learning techniques. © 2023 IEEE.

关键词： Music

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Advancements in Left Ventricular Myocardium Analysis: A Comprehensive Review of Myocardium Segmentation, Fibrosis Detection and Wall Motion Estimation Using Cardiac Magnetic Resonance Imaging

Advancements in Left Ventricular Myocardium Analysis: A Comp...

引用

2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Sunanda, H.G. Kavitha, K.S. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru560004 India Visvesvaraya Technological University Belagavi590018 India Dayananda Sagar College of Engineering Dept. of Computer Science and Engineering Bengaluru560078 India

ISBN: (纸本)9798350372502

Cardiac Magnetic Resonance Imaging (CMRI) has established itself as a cornerstone in cardiovascular diagnostics, enabling precise assessment of left ventricular (LV) myocardium. This review meticulously examines the multifaceted aspects of LV myocardium analysis, focusing on three pivotal components: myocardium segmentation, fibrosis detection, and wall motion estimation. By exploring cutting-edge techniques and methodologies in these domains, this article sheds light on the profound impact CMR has had on the accurate diagnosis, prognosis and structuring treatment plans for diseases of the heart. In the realm of cardiac diagnostics, Left Ventricular (LV) myocardium analysis utilizing CMRI has undergone a transformation advancement. This review encapsulates the comprehensive exploration of LV myocardium analysis, Myocardium segmentation techniques, ranging from traditional methods to contemporary machine learning-driven approaches, are scrutinized for their accuracy and efficiency. The detection and characterization of myocardial fibrosis, crucial in diagnosing various cardiac conditions, are dissected through cutting-edge CMR techniques such as Late Gadolinium Enhancement (LGE) imaging and quantitative mapping methods. Additionally, advanced methods of wall motion estimation, including feature tracking and strain imaging, highlighting their applications in assessing myocardial function. Real-world clinical scenarios underscore the transformation impact of these techniques on diagnosing heart diseases, guiding treatment decisions, and improving patient outcomes. The current landscape of LV myocardium analysis, emphasizing the pivotal role of CMR in revolutionizing cardiac healthcare. © 2024 IEEE.

关键词： Motion estimation

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Border Detection for Fishermen Using IoT 4

Border Detection for Fishermen Using IoT

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Divya, B. Vijayalakshmi, K. Alivn Ancy, A. Priyadharshini, J. NaveenKrishna, B.B. Harshavardhan, R. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Chennai Institute of Technology Dept of Electronics and Communication Engineering India Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508845

At present, the marine security issue includes the protection of fishers and address the rescue in case of illegal border infiltration. To detect the border for fisherman, this study conducted a method of enhancement in border detection essentially utilising the Automatic Identification System (AIS) with the assistance of the Internet of Things (IoT). In the middle sea, to provide boat detection with high precision, the proposed model incorporates Very High Frequency (VHF) communication inspired by X-band radar. The system is able to detect and monitor the boats at sea. To provide the middle sea with a high level of detection and monitoring of boats, system architecture included VHF (Very High Frequency) communication, which was inspired by the X band radar system. The range of AIS was increased using VHF communication. The coastal monitoring stations receive the position and status data from fishing boats with the help of IoT devices. The new AIS technology includes IoT-enabled devices installed on fishing boats. The IoT devices situated on boats send information to coastal monitoring station after sending data. The advanced technology increases the software capabilities of AIS, which will be helpful in the situation where the only AIS information was not enough. The advanced software capabilities improved the range of AIS and the definition capability of the vessels which were identified in the ocean near borders. The Automatic Identification System (AIS) that is being used in the present day has been upgraded and the Internet of Things (IoT) has been introduced. As a result, the system is now capable of much more comprehensive and real-time tracking and monitoring, as well as communication for marine vessels. This combination of technologies strengthens the safety, efficiency, and management of maritime operations by giving ships the Opportunity to connect and exchange data with different sensors, devices, and networks, and thus offering a more detailed and dynam

关键词： Fishing vessels

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Offline Mathematical Expression Evaluator using Non-Linear SVM

Offline Mathematical Expression Evaluator using Non-Linear S...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Gogula, Susmitha Valli Reddy Pittala, Venkata Dheeraj Chirala, Ashrith Mantena, Krishnam Raju Pillai, Ashwin Raj Gokaraju Rangaraju Institute of Engineering and Technology Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350308167

The Offline Handwritten Expression Evaluator [OHMEE] is a Machine Learning Model used to recognize and solve the Offline Handwritten Mathematical Expressions [OHME]. The term Offline refers to documents, images, papers and transcripts. We perform Image Pre-processing steps like Image Segmentation, Character Segmentation, Normalization and Binarization to covert the Image data to appropriate training data for the model using OpenCV Library. The character images in the [OHME] are classified and recognized using Non-Linear Support Vector Machines [SVM] and they are converted to appropriate Strings. These Strings are solved using user-defined mathematical methods. These modules are used to evaluate these String expressions and the appropriate results are generated and plotted. © 2023 IEEE.

关键词： Binarization Character Segmentation Image Segmentation Normalization OHME OHMEE OpenCV SVM

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Optimizing Crop Yield through A Data-Driven Approach with IoT Data Analytics and Machine Learning Algorithms 15

Optimizing Crop Yield through A Data-Driven Approach with Io...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Moeed, Syed Abdul Jinukala, Akshaya Kolipaka, Bhavana Kalvala, Aakanksha Kusuma, Vijay Kumar Kakatiya Institute of Technology and Science Dept of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350370249

Agriculture has long been the backbone of the Indian economy, defining the country's social and cultural milieu. Some of the most common difficulties that farmers face includes selecting suitable crops for their area and utilizing the necessary fertilizers, which can lead to a drop in production. To address these challenges, precision agriculture is employed. Precision agriculture is a smart and modern farming methodology that uses IoT-based research to provide information on soil qualities, types of soil, and crop production statistics. This assists farmers in choosing the optimum crops to plant and provides fertilizer recommendations based on specific location characteristics, also predicting the likelihood of plant diseases. In this project, we introduce a recommendation system by applying machine learning models such as Support Vector Machine (SVM), Logistic Regression, Decision Tree, Random Forest, and Naïve Bayes on a crop dataset, which is entirely IoT-based. The system recommends the best crop for the location's particular parameters with the highest accuracy. The system achieved an accuracy of 99% for crop recommendation and 92% for plant disease prediction. This research utilizes soil nutrients and pH data as inputs to create a website that predicts the most suitable crops for a particular soil type and suggests appropriate fertilizers. Additionally, the system predicts plant diseases and provides information about the diseases, including suggested cures. Thousands of farmers will be able to access the system through a Flask-based web interface. © 2024 IEEE.

关键词： Crop Recommendation Data Analytics (DA) Fertilizer Recommendation Internet of Things (IoT) Machine Learning (ML) Plant disease prediction Precision Agriculture ResNet

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A Comprehensive Review of Deep Learning Methods for Hyperspectral Image Compression 3

A Comprehensive Review of Deep Learning Methods for Hyperspe...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Afrin, Afsana Mamun, Md. Al Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350388282

Rapid advancement in the development of hyperspectral image (HSI) sensors has significantly enhanced the capabilities of capturing detailed spectral information across a wide range of wavelengths, enabling more precise analysis and interpretation of the observed scenes. Managing the storage of these extensive images poses a crucial challenge, requiring the application of compression techniques. Deep Learning (DL) algorithms have seen a massive rise in popularity over the past few years for HSI compression as well as HSI analysis. This review paper provides a comprehensive summary of the current literature on deep learning methods for HSI compression. Additionally, it explores the challenges, opportunities, and potential directions for future research in HSI compression. © 2024 IEEE.

关键词： Image enhancement

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