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3rd International Conference on Intelligent Communication and Computational techniques, ICCT 2023

作者： Shirgur, Pranav Chaurasia, Sandeep Manipal University Jaipur B. Tech Computer Science and Engineering Jaipur Rajasthan India Manipal University Jaipur Department of Computer Science and Engineering Jaipur Rajasthan India

ISBN: (纸本)9781665453578

The Internet of Things (iot) is a rapidly developing field of technology which entails a network of smart devices connected to each other and the internet. The TATCRb2industry is anticipated to increase by 18% to 14.4 billion active connections in 2022. There will likely be about 27 billion linked iot devices by 2025 as supply limitations, brought about by the current global semiconductor and chip shortage - loosen and demand quickens. iot has quickly penetrated the healthcare industry, this paper defines a framework that enables the development of secure and scalable iot healthcare platforms/applications. These platforms will also allow for secure cloud storage and analysis of patient data, helping professionals recognize latent parameters such as patient behavioral patterns that contribute to an ailment. This ultimately will enable the study of social and economic impact of a particular disease. This will greatly cull the survivorship bias in the health care industry - especially in testing times like a pandemic. © 2023 IEEE.

关键词： Health care

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Parametrized Optimization based on an Investigation of Musical Similarities Using SPARK and Hadoop

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SN computer science 2024年第1期5卷 78页

作者： Chaudhary, Savita Karthik, V. Shankar, R. Taranum, Ayesha Naresh, E. Department of Computer Science and Engineering Sir M Visvesvaraya Institute of Technology Bengaluru Bengaluru India Department of Information Science and Engineering Ramaiah Institute of Tech B’lore India Department of Computer Science and Engineering BMS Institute of Technology and Engineering Bengaluru India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The big data processing framework Spark is used to power a parameterizable recommender system that can make recommendations for music based on a user’s individual tastes and take into account a variety of musical tonal qualities. The system as it is presently built is completely scalable, which means that additional songs can be contributed to the data, the cluster size could be increased, and new types of audio information, in addition to more cutting-edge similarity evaluations, may be included. Another issue discussed in this research paper is the parallel collection of required audio characteristics on a computer cluster. Song recommendations for a dataset including more than 114,000 songs may be created on a Spark cluster with 16 nodes in under 12 s by integrating eight distinct audio feature types and similarity assessments. After the features have been retrieved, they are sent to the Spark-based recommender system to be processed. The calculated distance was displayed, examined, and graphically depicted. by computing the distance depending on the melody, rhythmic, and timbral components of the music, the final software controls song suggestion. © 2023, The Author(s).

关键词： big data Hadoop Music information retrieval Optimization Spark

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Fundamental Limits of Doppler Shift-based, ToA-based,and TDoA-based Underwater Localization

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1637-1639页

作者： Zijun Gong Cheng Li Ruoyu Su Hong Kong University of Science and Technology(Guangzhou) IOT ThrustGuangzhou 511400 Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyHong Kong 999077China Faculty of Engineering and Applied Science Memorial University of NewfoundlandNewfoundland A1B 3X5Canada School of Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China

Dear Editor,This paper is concerned with the underwater localization based on acoustic signals. Specifically, we will focus on the search of an underwater target that can constantly broadcast a beacon signal, such as a black box. Common measurements for localization are Doppler shift [1], time of arrival(ToA) [2]–[4], time difference of arrival(TDoA) [5], [6], angle of arrival(AoA) [7], etc.

关键词： TDoA ToA underwater

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Implementing blockchain based Security in EHR Using Ganache 6

Implementing Blockchain Based Security in EHR Using Ganache

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Satyam, Kumar Sharma, Ayush Devi, Runumi B.Tech in Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India Galgotias University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

A blockchain-based electronic health record (EHR) system has been proposed to manage patient data and maintain computerized records across different healthcare institutions. The main objective of this system is to provide immediate access to patient health information while addressing concerns about privacy and security. It is developed on the Ethereum network using programming languages, tools, and technologies such as Ganache, Solidity, and ***. The system uses smart contracts to provide security and privacy, and transactions are verified and propagated to the entire decentralized network. The system includes a cryptocurrency wallet (MetaMask) with a centralized privacy system that allows authorized parties to access and secure records quickly. The proposed system aims to achieve several objectives such as allowing users to receive the same data simultaneously, increasing efficiency, building trust, and reducing barriers. It secures data storage by restricting user access and enables the secure transfer of patient medical records. The solution allows for better transparency and ownership of sensitive data, which can be recorded and safeguarded via blockchain technology, therefore assisting the healthcare sector. This paper describes a medical record system and a new protocol that can be used quickly and safely, paving the way for more secure and efficient healthcare systems. Overall, the proposed system offers potential solutions to the challenges associated with traditional EHR systems and has the potential to revolutionize the healthcare industry. © 2023 IEEE.

关键词： blockchain Electronic Health Record Ethereum Ganache IPFS MetaMask

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Digital Cottage Hub for Empowering Artisans, Fostering Connectivity, and Promoting 'Make in India' 2

Digital Cottage Hub for Empowering Artisans, Fostering Conne...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Hiremani, Vani Praveen Gujjar, J. Shinde, Sumit Devadas, Raghavendra M. Anser Pasha, C.A. Ambreen, Lubna Pune Campus Lavale Pune412115 India Bengaluru India B. Tech Computer Science and Engineering ITM Skills University Navi Mumbai India Bengaluru India Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (纸本)9798350383867

Aims to redefine the landscape of the cottage industry with an innovative social web application designed to simplify the exchange of handcrafted items. At its heart, the platform aims to bridge the gap between producers and customers by providing a thriving marketplace for one-of-a-kind, handcrafted things. With a focus on encouraging job possibilities, particularly for women, the application corresponds with the wider purpose of supporting "women empowerment" and contributing to national efforts such as "Make in India. "The system study highlights the existing holes in present systems, emphasizing the necessity for a complete platform tailored exclusively to the cottage sector. An architecture diagram and extensive use case overviews for customers, producers, and administrators are included in the system design. Implementation specifics include login, identity, purchasing, and administrative operations, among others. The initiative aims to promote not just economic activity in the cottage industry, but also social issues like as empowering women and inclusive employment. The dedication to ongoing development is obvious in the plans for the future, which include tightening security measures, notably for online financial transactions. Finally, the social web application aspires to build a dynamic and safe digital area for artisans, producers, and customers, therefore leaving a lasting impression on the cottage industry in the age of technology. The chatbot is implemented using the rasa open-source framework along with the digital platform. The results of the chatbot are discussed in detail in the results and discussion section. © 2024 IEEE.

关键词： Sales

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DLMNN based Heart Disease Prediction with PD-SS Optimization Algorithm

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Intelligent Automation & Soft Computing 2023年第2期35卷 1353-1368页

作者： S.Raghavendra Vasudev Parvati R.Manjula Ashok Kumar Nanda Ruby Singh D.Lakshmi S.Velmurugan Department of Information and Communication Technology Manipal Institute of TechnologyManipal Academy of Higher EducationManipal576104India Department of Information Science and Engineering SDM College of Engineering and TechnologyDharwad580002India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering B.V Raju Institute of TechnologyTelangana502313India Faculty of Engineering and Technology SRM Institute of Science and TechnologyNCR CampusUttar Prasesh201204India School of Computing Science and Engineering VIT Bhopal UniversityMadhya Pradesh466114India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

In contemporary medicine,cardiovascular disease is a major public health *** diseases are one of the leading causes of death *** are classiﬁed as vascular,ischemic,or *** information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart *** failure rates have risen drama-tically in recent years as a result of changes in modern *** diseases are becoming more prevalent in today’s medical *** year,a substantial number of people die as a result of cardiac *** primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of *** improve the efﬁciency of the classiﬁcation algo-rithms,we construct a data pre-processing stage using feature ***-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modiﬁed Neural Network(DLMNN)model combined with the Pet Dog-Smell Sensing(PD-SS)algorithm predicted the highest classiﬁcation performance on the UCI Machine Learning Heart Disease *** DLMNN-based PDSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease *** demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease *** proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.

关键词： Machine learning random forest coronary heart disease cardiovascular optimization algorithm

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A Novel Mobile Application to Empower Visually Impaired Individuals to Perform Independent Day-to-Day Activities 8

A Novel Mobile Application to Empower Visually Impaired Indi...

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8th International Conference on I-SMAC (iot in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Paul, Dewanshi Patel, Ankit Kumar Dhanush, R. Amutha, S. Vellore Institute of Technology B.Tech Electronics and Computer Engineering School of Electronics Engineering Chennai India Vellore Institute of Technology School of Electronics Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350376425

People with visual impairments struggle or require assistance from others to perform day-to-day activities. Hence, people with visual impairments suffer from a lack of independence and quality of life. For this purpose, the app proposed in the paper is a unique, creative, innovative mobile application that will aid people with visual impairments in becoming independent and improving their quality of life. This paper introduces the novel mobile application's three key features: QR (Quick Response) code scanning, OCR (Object Character Recognition), and an Indoor Object Navigation System. The QR code scanning feature allows users to make informed decisions quickly by providing comprehensive information about products or their environment. The OCR functionality reads out the printed text aloud and helps the users understand written content with the help of audio. The indoor object finder or navigation system is a noteworthy advancement in Assistive technology. The system allows users to select a target object and then move their camera around their surroundings;once the object is detected, the app provides voice instructions about the directions of the target object to guide users towards it. by integrating these complex technologies, the novel mobile application provides an innovative approach to some of the issues encountered by those with visual impairments. The application aims to increase user independence, improve functionality, and build confidence to improve their daily experiences and engagements with the world around them. © 2024 IEEE.

关键词： Navigation systems

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Adaptable Individualized Investment Recommendation System

Adaptable Individualized Investment Recommendation System

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Sabiyath Fatima, N. Noor Alleema, N. Mahesh, C. Umanesan, R. Senthil, K. Santhosh Kumar, P. B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Information Technology Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798331522988

Internetization has played a massive role in the rapid development and growth of the financial investment industry. Due to the rapid development, there is a demand for more financial knowledge services to help make well-informed investments. Accessing and conducting investments through online financial platforms has made the field highly accessible. This has made the investment recommendation system a popular research area. Personalized recommendations will help the user make investments that are safe for their financial situation and also suit their particular needs and interests. This work proposes a method where investment recommendations are given based on three key factors. The user interest sectors, risk appetite-based clustering, and market sentiment of the stock. These factors will be calculated by the use of Lasso regression, SVM, stepwise regression, generalized boosted regression, random forest, ridge regression and fuzzy clustering. based on these three factors, the proposed method results in investment recommendations made for users that are personalized to the specific user. This allows existing investors to gain new opportunities and new investors to start investing with lower amount of risk using the information available. © 2024 IEEE.

关键词： Decentralized finance

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Enhanced iot System for Real-Time Detection and Monitoring of Cardiac Events Using Machine Learning Algorithms 3

Enhanced IoT System for Real-Time Detection and Monitoring o...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Safia Naveed, S. Ameen, Nabeena Nulin Jeriba, J. Kcg College of Technology Department of Computer Science and Engineering Chennai India B. S. Abdur Rahman Crescent Institute of Science and Tech Department of Information Technology India

ISBN: (纸本)9798331518592

Heart attacks remain a primary cause of global mortality, highlighting the urgent need for systems that enable early detection and intervention. This study proposes a Smart iot-based system designed to detect and monitor heart attack symptoms in real-time, thus facilitating timely medical response. The system integrates wearable sensors to collect continuous data on heart rate, stress levels, and positional changes, which is then processed through Arduino IDE and transmitted to an iot server. Machine learning algorithms, including Support Vector Regression (SVR) and Artificial Neural Networks (ANN), are employed to analyze these signals for abnormal patterns indicative of a potential heart attack. Major findings indicate that this system accurately detects heart rate irregularities, and stress fluctuations and can alert healthcare providers, significantly improving remote monitoring capabilities. The implications of this study suggest that iot-enabled monitoring systems can substantially enhance patient outcomes by allowing for early diagnosis and proactive care in remote settings, thereby reducing the risk of fatal heart events. © 2024 IEEE.

关键词： Wearable sensors

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Optimized Deep Learning Mechanism for Intrusion Detection: Leveraging RFE-based Feature Selection and PCA for Improved Accuracy 5

Optimized Deep Learning Mechanism for Intrusion Detection: L...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rajkumar, M. Vs, Lakshi Karthik, R. Pavithra, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Smart Computing Tamil Nadu India B. Tech-Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331530013

With the growing demand for wireless networks, network traffic has surged, particularly involving large-scale data transmission. This results in an increased number of network security threats and the emergence of various cyber-attacks. To overcome these threats, the development of an efficient intrusion detection system (IDS) has become crucial. by leveraging the recent advancements in Artificial intelligence (AI), this project introduces an optimized deep learning mechanism utilizing a prevalent deep learning model, the Deep Neural Network (DNN) algorithm. This proposed method uses feature engineering techniques like feature selection and feature reduction. Given the high dimensionality of the selected datasets, these techniques are employed to prevent over-fitting. Recursive feature elimination performs feature selection followed by Principal Component Analysis for the feature reduction process, thus enhancing the model's efficiency. The system is assessed using two benchmark datasets: KDD and UNSW-Nb15. The experimental results indicate that the presented DNN model on the KDD dataset has achieved 92.45% accuracy and 91.35% accuracy on the UNSW-Nb15 dataset for intrusion classification. © 2024 IEEE.

关键词： Network intrusion

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