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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Shweta Sinha Tushar Agarwal Pratiyush Pandey Computer Science and Engineering Amity university India Amity school of engineering and technology Amity university India Amity School of Engineering and Technology Amity University India

ISBN: (纸本)9798400709418

Artificial Intelligence has made significant contributions in various aspects and domains of human life, permeating fields such as Education, Healthcare, Economy, Socialization, Security, Agriculture, and Privacy. This research specifically delves into the promising possibilities and applications of AI in healthcare and medical domains, focusing on developing a Medical Named Entity Recognition (NER) model. The proposed approach involves using different pre-trained NER models and an in-house-generated corpora to train a personalized Spacy model. The research then conducts a thorough comparison of these models to understand their strengths and shortcomings. Widening the scope of the proposed research, we introduce an inventive mechanism to extract medical information from doctors' audio in real-time saved on buffer memory, deleted after use, which is then used to create patient medical prescriptions. The potential benefits of this mechanism are vast, expanding beyond reducing patient wait times, enhancing the clarity of prescriptions, maintains comprehensive patient histories, and finally introducing automation into the prescription drug ordering chain. Additionally, the proposed model eases the scheduling and management of medical tests and procedures based on doctor and patient availability. In jist, the proposed paper aims to present an innovative human-centric approach to automating interactions between patients and doctors and optimizing the delivery of healthcare services. Through the integration of modern-day AI services, The proposed mechanism has the potential to bring about a potential change in medical practices, providing a more efficient and effective breakthrough to patient care.

关键词： AI in Healthcare Artificial Intelligence Named Entity Recognition Natural Language Processing

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Data-Driven Healthcare Management, Analysis, and Future Trends

Data-Driven Healthcare Management, Analysis, and Future Tren...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Das, Lipsa Kumar, Ankesh Sharma, Vikrant Bhatnagar, Devang Singh, Sumit Tripathi, Nandita Amity School Of Engineering & Technology Amity University UP Greater Noida India Amity University UP Greater Noida India Computer Science And Engineering Graphic Era Hill University Graphic Era Deemed To Be University Dehradun India Amity Institute Of Information Technology Amity University UP Greater Noida India Allied Sciences Amity University UP Greater Noida India

ISBN: (纸本)9798350366846

Data-driven approaches are revolutionizing healthcare management and analysis, offering insights into patient care, operational efficiency, and future trends. The sum of information Big data' is, can do miracles. In the last 20 years, it has become a topic of interest because the potential lies within is enormous. Big Data is produced, stored and examined by several industries of public and private domain with the main aim to enhance their services. Big Data comes from variety of resources in medical industry such as medical patient records, test reports, and IOT medical devices. The large amount of data is generated by medical research which is vital to public healthcare. The data must be appropriately organised and analysed to obtain useful information. Else, finding solution through big data analysis will be like looking for needle in haystack. Set of difficulties frequently occur at every phase of managing Big Data which can only be overcome by installing advanced computer systems for its analysis. This paper examines the relationship between data science and healthcare with an intent to use complex metrics to make intelligent and accurate decisions. Important areas are data-driven approaches for enhancing medical administration, recognizing new developments influencing healthcare evolution, and the methods for evaluating large sets of medical data. By incorporating the data-driven techniques, the medical industry might able to improve the patient health, optimize operations and adjust to current as well as to continuously evolving situations. © 2024 IEEE.

关键词： Big data

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Keypoint Detection for Identifying Body Joints using TensorFlow 4

Keypoint Detection for Identifying Body Joints using TensorF...

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4th International Conference on Information Management and Machine Intelligence, ICIMMI 2022

作者： Grover, Anuj Arora, Deepak Grover, Anant Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Lucknow Campus India

ISBN: (纸本)9781450399937

Keypoint Detection for the Body Joints is a process of finding and detecting all the important body joints of a human being. It takes an RGB image as input gives a list of keypoints as output. It can be applied for single-person body joints detection or multi-person body joints detection. The various architectures for detecting the human body joints can be compared using the Percentage of Detected Joints (PDJ), Object Keypoint Similarity (OKS) and mean Average Precision(mAP) evaluation metrics. The state-of-the-art body joint detection architectures like DeepPose, Higher Resolution Network and OpenPose architectures were studied and compared against each other. These architectures were evaluated on the COCO dataset for the 17 key body joints using the OKS and mAP evaluation metrics. The evaluation results on all the body joints were compared to each other for each architecture. The architectures were implemented using opensource implementations of the PyTorch and TensorFlow libraries. The HRNet architecture was the fastest and most accurate of all the architectures. © 2022 ACM.

关键词： Network architecture

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Heart Disease Analysis and Prediction Using EDA and ML Classifiers 23

Heart Disease Analysis and Prediction Using EDA and ML Class...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Humra Khan Pawan Singh Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University India

ISBN: (纸本)9798400709418

Heart is one of the most important organs as blood is pumped throughout the body by the heart. Heart disease is widely regarded as the illness that kills individuals most quickly. To prevent catastrophic risks and diminish heart-related problems, prompt identification of heart disease is essential. The disease prediction system helps clinicians identify serious illnesses as early as possible. This research paper aims to provide a complete examination of the features contributing to heart disease with respect to by means of exploratory data analysis and predict the possibility of heart disease with the use of machine learning algorithms. Through EDA, it was discovered that, of all these features, the number of major vessels (0–3) stained by fluoroscopy, different types of chest pain, greatest heart rate reached, exercise-induced angina, exercise-induced ST depression compared to rest, and inclination of the peak exercise ST segment are the ones that have the greatest impact on the diagnosis of heart disease. Utilising the UCI heart disease dataset containing the features, three machine learning classifiers are implemented: decision tree (DT), random forest (RF), and K-nearest neighbour (KNN). When compared to other heart disease prediction models, Random Forest has the best rate of accuracy (85.25%).

关键词： Exploratory Data Analysis (EDA)

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Forecasting Continuous Air Pollution with Cloud-Based Big Data Platform using Spatial-Temporal Visualization 8

Forecasting Continuous Air Pollution with Cloud-Based Big Da...

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8th IEEE International Conference for Convergence in technology, I2CT 2023

作者： Chang, Yue Shan Abimananan, Satheesh Shukla, Saurabh Satheesh, Dhivyadharsini National Taipei University Department of Computer Science and Information Engineering New Taipei City Taiwan Amity University Maharashtra Department of Computer Science and Engineering Mumbai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350334012

In today's era of rapidly growing data, having a well-designed platform for analyzing and visualizing big data is becoming increasingly important. This study presents a cloud-based big data platform for analyzing and visualizing continuous spatial-temporal air pollution. The platform combines a Spark Hadoop-based machine learning environment with a TensorFlow-based deep learning framework for forecasting and a map for demonstration and visualization of the results. The platform uses the Inverse Distance Weighted (IDW) method to estimate the concentration of particulate matter in the atmosphere and a cloud based non-SQL database to store the pre-calculated air quality images. The platform also features a sliding window mechanism, which allows users to easily visualize the results over time. This work provides an instant overview of air quality in Taiwan and evaluates the performance of the platform, concluding that it is efficient and capable of providing continuous spatial-temporal results. This platform represents a significant step forward in the field of air quality monitoring and forecasting. © 2023 IEEE.

关键词： Data visualization

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A Provably Secure Lightweight Key Agreement Protocol for Wireless Body Area Networks in Healthcare System

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1683-1690页

作者： Zia, Maryam Obaidat, Mohammad S. Mahmood, Khalid Shamshad, Salman Saleem, Muhammad Asad Chaudhry, Shehzad Ashraf Comsats University Islamabad Department of Computer Science Sahiwal57000 Pakistan University of Jordan King Abdullah Ii School of Information Technology Amman11942 Jordan University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Amity University UP Noida201301 India National Yunlin University of Science and Technology Future Technology Research Center Yunlin64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Abu Dhabi University Department of Computer Science and Information Technology College of Engineering Abu Dhabi United Arab Emirates Nisantasi University Department of Computer Engineering Faculty of Engineering and Architecture Istanbul34398 Turkey

Wireless Body Area Network (WBAN) is a vital application of the Internet of Things (IoT) that plays a significant role in gathering a patient's healthcare information. This collected data helps special professionals like doctors or physicians analyze patients' health status to cure different diseases. However, collecting such information from an insecure channel can be threatening due to the potential security threats. Therefore, it is crucial to secure this sensitive information. This article proposes a secure and lightweight authentication protocol for WBAN. The devised protocol is scalable, secure, and lightweight compared to various relevant competing protocols. The informal security analysis shows that the designed protocol is lightweight, secure, and efficient in resisting various major attacks. The performance analysis demonstrates our protocol's supremacy over various competing protocols in terms of computation and communication costs, inducing efficiency of 20.3% and 12.3%, respectively. Moreover, the practical performance of the designed protocol from the network point of view is measured using the widely recognized NS3 simulation tool. © 2005-2012 IEEE.

关键词： Authentication

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A robust blockchain-based watermarking using edge detection and wavelet transform

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Multimedia Tools and Applications 2025年第14期84卷 12739-12763页

作者： Mannepalli, Praveen Kumar Richhariya, Vineet Gupta, Susheel Kumar Shukla, Piyush Kumar Dutta, Pushan Kumar Chowdhury, Subrata Hu, Yu-Chen G H Raisoni Institute of Engineering and Technology Nagpur India LNCT University Bhopal India University Institute of Technology RGPV Bhopal India School of Engineering and Technology Amity University Kolkata Kolkata India AP Chittoor India Department of Computer Science Tunghai University No. 1727 Sec. 4 Boulevard Xitun District Taichung407224 Taiwan

Nowadays, image security is a crucial component of the scientific community. The transparent marking is often used to keep medical imaging information from being stolen without permission. In this work, a novel combination of blockchain-based encryption and invisible picture watermarking was presented. In order to achieve watermarking, edge detection of discrete wavelet transform coefficients is being used. Using the discrete wavelet transform to break up the medical image gives you multi-resolution coefficients. For the HH wavelet band, the edge detecting method is employed to make the edge coefficients. For watermark embedding, the difference between dilation and edge coefficient is used in order to enhance robustness. The image with the watermark is encrypted by employing a hashing process that is based on blockchain technology and was developed particularly for medical pictures. After the decryption procedure is finished at the conclusion of the decoding process, the picture is then regenerated. The results of both stages are presented in a sequential order. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Enhancing Microgrid Stability: Optimal BESS Sizing via Modified PSO for Frequency Control and Cost Efficiency 7

Enhancing Microgrid Stability: Optimal BESS Sizing via Modif...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Singh, S. Vikram Revathi, V. Babu, B Sankara Bhavana, Munugapati Singh, Navdeep Nagamani, G. Amity University Amity School of Engineeringh and Technology Uttar Pradesgh Greater Noida India New Horizon College of Engineering Department of Applied Sciences Bangalore India Griet Department of Computer Science and Engineering Bachupally Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

When constructing a Battery Energy Storage System (BESS) to be incorporated in a microgrid, the main objective is to minimise the risk of microgrid stability and system failure. An improper or careless sizing of the BESS may lead to system losses, excessive capacity, and high installation costs. Consequently, this study suggest a novel technique for managing the frequency of the independent microgrid using an improved particle swarm optimisation (PSO), which aims to find the optimum size of the BESS while minimising its total cost. Our research focuses on finding the most cost-effective way to deploy BESS in microgrids for a period of 15 years. We will also recommend the optimal measurements of BESS to minimise microgrid volatility and system failure in the case of utility grid outage. We will use the PSO approach for frequency regulation. The next step is to examine a standard microgrid and evaluate BESS in comparison to other existing storage systems in terms of their economic performance. By comparing the optimum size of BESS- modified PSO method to both the conventional size of BESS and the ideal size of BESS-based empirical approach, the findings show that the latter may improve the dynamic system effectiveness. In a typical microgrid, the polysulfide-chromium BESS is predicted to be cheaper throughout the 15 years of deployment compared to the chrome redox BESS, when evaluating the financial health of the two storage techniques. Findings indicate that microgrid planning flexibility, grid security, and power system stability may all be greatly enhanced using the suggested PSO method-based frequency management. Simultaneously, it is able to meet the needs for frequency control economically. © 2024 IEEE.

关键词： Cost effectiveness

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Proposed AES-based Obfuscation and Encryption Model for Cloud Data Security

Proposed AES-based Obfuscation and Encryption Model for Clou...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： Prabhdeep Singh Pawan Singh Abhay Kumar Agarwal Anil Kumar Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow India Department of Computer Science and Engineering Kamla Nehru Institute of Technology Sultanpur India Amity School of Engineering and Technology Amity University Lucknow India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Cloud Computing (CC) offers a diverse range of services along with huge data storage across a network. CC has collaborated with varied emerging technologies like IoT because of its numerous advantages. Despite CC's benefits, cloud-based servers are not always trustworthy in real-world scenarios. Users' privacy will be compromised if they send their data straight to cloud platforms since crucial information will be exposed to cloud servers. Thereby, this work develops a data security model in the cloud with two stages, proposed secured data storage and proxy re-encryption. Initially, the data is stored securely in the cloud based on improved AES-based encryption as well as obfuscation-based encryption. Based on the request from the data user, proxy re-encryption is done using Kronecker product-based encryption. The enhanced AES achieved the lowest value of 0.025 for the CCA attack with a key size of 128 bits. Thus, the final encoded key is obtained.

关键词： Cloud computing Data privacy Quantum computing Memory Elliptic curve cryptography Data models Encryption Servers Intelligent systems Homomorphic encryption

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Integrating Renewable Energy Sources into Smart Grids with an Aggregator-Based Energy Management System for Efficiency and Resilience 7

Integrating Renewable Energy Sources into Smart Grids with a...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kumar, B. Santhosh Devi, Anusuya Sharma, Smita Bhavana, Munugapati Babu, B. Sankara Singh, Navdeep Institute of Aeronautical Engineering Department of Cse Ai and Ml Telangana Hyderabad India New Horizon College of Engineering Department of Applied Science Karnataka Bangalore India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Griet Department of Computer Science and Engineering Bachupally Hyderabad India Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350350067

Integrating renewable energy sources (RESs) into smart grids (SGs) is challenging due to the intermittent and unpredictable nature of the sources, which are often the sun or wind. Additionally, it is important to think about how to get customers involved in the electricity sector so that we can lower energy prices globally. According to this article, an energy management system (EMS) may help the SG run more efficiently. The EMS aggregates distributed energy resources to make SG market participation easier. Integrating demand-side management (DSM) with active management strategies (AMS) has financial and environmental benefits as it increases the use of renewable energy sources while decreasing consumer energy costs. By incorporating their preferences and costs into market processes, energy supply/demand and distribution system operators (DSOs) may respond to real-time price signals. This has the potential to make the grid more adaptable and resilient. To verify the effectiveness of the proposed EMS, a 23-bus, 11-kV transmission network is used. © 2024 IEEE.

关键词： Clean energy

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