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Smart Detection of Passive Smokers using Machine Learning 8

Smart Detection of Passive Smokers using Machine Learning

引用

8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Shah, Pratham Parekh, Astha Bajaj, Divyansh Swain, Debabrata Kumar, Manish Bhilare, Amol Pandit Deendayal Energy University Department of Computer Science and Engineering Gandhinagar India Pandit Deendayal Energy University Department of Electronics and Communication Engineering Gandhinagar India Vishwakarma Institute of Technology Computer Engineering Department Pune India

ISBN: (纸本)9798350391770

Smoking is a destructive and addictive practice of inhaling burning plant material. It produces nicotine in the bloodstream that creates a negative impact on the bones, hormones, DNA, eyes, and the immune system. This has become the source of a great concern, especially amongst the young generation. Machine Learning is used to find a pattern of biological attributes to identify possible smokers. The process of teaching a machine to do intelligent classification from a sizable archive of historical data is known as machine learning. The aim of this paper is to develop a learning model that can detect passive smokers through features like eyesight, weight, fasting blood sugar, cholesterol and more. On this dataset, a number of data preprocessing operations are carried out to build a reliable model which include checking for null values , encoding the data , balancing the dataset and scaling it and selection of highly correlated features. A variety of supervised learning models are employed in the model training process to create a robust machine learning model. From the set of models applied, K-Nearest Neighbour (KNN) (84 %) and Random Forest (RF) (86 %) gave the highest accuracy. However, implementing Random Forest with Hyperparameter tuning using GridSearchCV resulted in a higher accuracy (87 %). © 2024 IEEE.

关键词： Decision trees

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Detection of Mental Stress using EEG signals - Alpha, Beta, Theta, and Gamma Bands 5

Detection of Mental Stress using EEG signals - Alpha, Beta, ...

引用

5th IEEE International Conference for Emerging technology, INCET 2024

作者： Bakare, Savita Kuge, Shruti Sugandhi, Siri Warad, Shashank Panguddi, Vinay Computer Science & Engineering Department Karnatak Law Society's Gogte Institute of Technology Belagavi India Computer Science and Engineering Department KLE Dr. M. S. Sheshgiri College of Engineering and Technology Belagavi India

ISBN: (纸本)9798350361155

Mental stress, or psychological stress, arises when individuals perceive emotional or psychological strain beyond their coping abilities. Various factors such as personal relationships, work pressure, financial problems, or major life changes, impact both emotional and physical well-being. Electroencephalography (EEG) signals serve as insightful indicators of brain activity, resembling tiny electrical whispers exchanged between brain cells. These brain cells are characterized by EEG patterns such as alpha, beta, theta, and gamma frequency band waves, and offer valuable insights such as frequency analysis, categorization of valence and arousal, and machine learning techniques, into distinct mental states and neural activity. The exploratory data analytics (EDA) techniques using ML methods (KNN, SVM, and RF) on EEG dataset is being performed to analyze mental stress detection. Furthermore, Mean Absolute Error (MAE) scores of the KNN, SVM, and RF models are compared based on the number of folds, which shows that KNN has the lowest MAE of 0.62, compared to SVM scores of 0.83 and RF score of 1.47 respectively. As a result, the KNN model is selected for stress detection due to its superior performance in reducing MAE in both iterations and training data sizes. Furthermore, the study concisely also reviews an existing literature on mental stress detection using EEG signals, highlighting prevalent challenges and research gaps. © 2024 IEEE.

关键词： Electroencephalography

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A GitHub Actions Workflow for Continuous Delivery of Software on Arduino-Based IoT Devices 8

A GitHub Actions Workflow for Continuous Delivery of Softwar...

引用

8th International Conference on Computing in engineering and technology, ICCET 2023

作者： Musitmani, Suthinan Chauhan, Naveen Department of Computer Science & Engineering National Institute of Technology Hamirpur Hamirpur India

ISBN: (纸本)9781839539237

The phrase "Internet of Things"(loT) describes a wide range of actual physical things or gadgets that collect, measure, and wirelessly transmit data about physical properties over the Internet. loT systems have their own software development life cycle. Because the system involves human interaction, the testing and deployment requirements for these systems are slightly different than usual. Therefore, a specialised continuous integration and delivery approach to releasing new software versions must be used. GitHub Actions help automate event-driven tasks and can be transferred between repositories, making it convenient for developers to simply build, test, and deploy any software project. The GitHub Actions workflow is suitable for loT software distribution because they can use it to automate the software loT system deployment process. This paper introduces over-the-air (OTA) software deployment algorithms, their implementation with a GitHub Actions workflow, and an example use case. The example use case demonstrates the ease of loT development using the proposed GitHub Actions workflow. The proposed algorithms therefore simplify the development of Arduino-based loT applications. © The Institution of engineering & technology 2023.

关键词： Internet of things

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Advanced Double Input Layered Neural Network for medical diagnosis by using cloud computing technology 1

Advanced Double Input Layered Neural Network for medical dia...

引用

1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Gowri, S. Jabez, J. Nirmalrani, V. Srinivasulu, Senduru Sathyabama Institute of Science & Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

Advanced Double Input Layered Neural Network has the potential to revolutionize medical diagnostics by solving pressing problems. It improves diagnostic precision by providing a single, unified platform for the examination of both organized and unstructured medical data. It provides real-time decision assistance and data management by utilizing the scalable, secure, and efficient data processing capabilities made possible by cloud computing. Improved patient care, more effective therapy, and higher-quality healthcare are some areas where Advanced Double Input Layered Neural Network can make a difference. Rapid, precise, and secure data analysis;the administration of multiple data sources;and realtime decision support are a few of the difficulties inherent in medical diagnosis. This research proposes an Advanced Double Input Layered Neural Network (ADILNN) with a double input layered neural community, enabling it to examine prepared and unstructured scientific data. This novel technique improves the community's mastering functionality from various data kinds. By centralizing statistics garage, processing, and analysis on the cloud, computing sources may be extra reliably accessed whilst wanted. The network's diagnostic precision and flexibility are each advanced through way of tool gaining knowledge of strategies (MLM). Because of its adaptability, ADILNN can be utilized in diverse medical fields, which include radiology, pathology, cardiology, and genetics. It permits examine genomic information, making recovery alternatives, and analyzing x-ray photos. The technique has numerous ability makes use of in healthcare, improving prognosis accuracy in diverse settings. Simulation analysis is used to gauge ADILNN's capacity by way of gauging its diagnostic accuracy, processing speed, scalability, and records protection. These research validate ADILNN's potential to improve clinical analysis, streamline facts management, and guarantee healthcare records's safe and effectiv

关键词： Diagnosis

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Artificial Neural Network and Game Theory for Secure Optimal Charging Station Selection for EVs

Artificial Neural Network and Game Theory for Secure Optimal...

引用

2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Kakkar, Riya Kumari, Aparna Gupta, Rajesh Agrawal, Smita Tanwar, Sudeep Institute of Technology Department of Computer Science and Engineering Nirma University Ahmedabad India

ISBN: (纸本)9781665494274

The penetration of electric vehicles (EV s) entails the deployment of more charging station (CS) infrastructure to realize the charging requirement issues of the EV s. But, limited installation of charging infrastructure and data security issues require a secure and efficient CS selection mechanism for EV s. Towards this goal, we proposed an Artificial Intelligence (AI) and game theory-based secure CS selection scheme for EVs using blockchain. Blockchain and AI-based proposed scheme provides security and privacy during the communication between participants, i.e., EV s and CSs, for optimal CS selection. Moreover, an incorporated blockchain network with Interplanetary File System (IPFS) strengthens the reliability and cost-efficiency of CS selection by using beyond 5G network and its ultra-intelligent features. Furthermore, the blockchain and AI-based proposed scheme utilizes coalition game theory approach to recommend the optimal CS for EV and balance the fair payoff between the participants in the network. Finally, experimental results show that the proposed scheme yields better results than the conventional approaches considering the performance evaluation metrics such as State of Charge (SoC), profit analysis, and latency comparison. © 2023 IEEE.

关键词： Blockchain

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Comparative Analysis of Crash Consistency Techniques in File Systems

Comparative Analysis of Crash Consistency Techniques in File...

引用

NIELIT's International Conference on Communication, Electronics and Digital technology, NICE-DT 2023

作者： Sondur, Archana Sakhare, Sachin More, Priyanka Department of Computer Engineering Vishwakarma Institute of Information Technology Kondhwa Pune India

ISBN: (纸本)9789819916986

Since it is necessary to manage user data, the file system is an essential part of the operating system. It must be reliable, efficient, and crash proof. For a variety of reasons, file systems are highly likely to experience a system crash or power outage, but with the help of a few crash consistency techniques, the data can still be assured to be crash consistent. To be able to create the required file system, a thorough understanding of these techniques has become highly critical. Hence, the authors have conducted a thorough survey to study crash consistency and its techniques. Using the relevant information that was within their reach, the authors conducted a comparative analysis of the three classic crash consistency techniques which are journaling, log-structuring, and shadow-paging using the factors like consistency achieved, performance, complexity, and availability and presented it in one table, and transaction method, storage, cost required, and main fault in another. With the aid of this study and the analysis, the authors have proposed an adaptive recommendation system to suggest to developers the crash consistency technique most suited for their file system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Digital storage

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Refining Pancreatic Cancer Detection and Classification through a Hybrid Approach: Stacked Deep Learning Integrated with the Sparrow Search Algorithm for Medical Image Analysis 2

Refining Pancreatic Cancer Detection and Classification thro...

引用

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

作者： Arunachalam, P. Devi, M. Kalpana Jose, Naduvathezhath Nessariose Vivekanandan, G. Sharma, Rinku Mahesh Yadav, Ganesh Kumar Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology MCA Department Sreenivasa Institute of Technology and Management Studies Chittoor Architect Denken Solutions Department of CSE Sri Sairam Institute of Technology Chennai Department of Computer Science and Engineering SVKM Institute of technology Dhule Department of Computer Science and Engineering JSS Academy of Technical Education

ISBN: (纸本)9798350383867

In the endeavour to improve the precision and efficiency of diagnosing and classifying pancreatic cancer, there is a new idea that fuses deep learning and the Sparrow Search Algorithm with a stacked structure. That is why we consider this approach as revolutionary in terms of utilizing medical image analysis for the diagnosis in the field of pancreatic oncology with more accurate results. Transforming using stacked deep learning allows the systemto recursively learn its own complex feature in medical images, which is a basis for the identification of cancer. SBA or Sparrow Search Algorithm has been developed based on this social sparrow model and is a separate optimization technique which helps in optimizing the method of classification in this case. The foremost aim of this particular paper is to incorporate these two strategies and use the endproduct to improve the diagnostics of pancreatic cancer and also theclassification of the same. The cyclic process is applied together with the stacked deep learning process as well as the Sparrow Search Algorithm to make the systems' continuous optimization for sensitivity and specificity in detecting pancreatic malignancies. It is well to remember that this is not just a pure technological innovationthat holds promise of changing the landscape of pancreatic diagnosisand therapy. This approach of diagnosis and treatment also suggests early diagnosis, develop individual management plan and likely to yield higher survival rates for such patients. In addition, it is also worth considering at the potential that has been provided in enhancement of all sorts of branches of medicine linked to image analysis, along with the potential that has been developed when the best of technologies is paired with the best of ideas in computation. In conclusion, this paper captures the notion of intentionality and exigency in medical procedures. This union of stacked deep learningwith the Sparrow Search Algorithm unveils a new epoch in the medic

关键词： Deep learning

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An Effective Public-key Authenticated Searchable Encryption for Protecting Sensitive data in Healthcare IoT

An Effective Public-key Authenticated Searchable Encryption ...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Shanthi, M. Navaneetha, M. Kartheek, G.C.R. Parameshachari, B.D. Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Gopalan College of Engineering and Management Department of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The practice of conveying medical records to either a Third-Party Cloud Service (TCS) for backups has become increasingly popular in recent decades at least as the healthcare Internet of things has expanded. However, when used in many environments, a few little PAEKS schemes also have security issues. People proposed a novel, efficient, but also effective PAEKS scheme in this article. The Diffie-Hellman pow consensus concept is used in this scheme to generate a secret key known only to the receiver and to the sender. We show that our scheme is completely risk-free against inside keyword various attacks in several co-environments when the Cloudera Diffie-Hellman assumption is used. From the analysis, it shows that proposed method achieved better results in terms of accuracy (96.8%), precision (95.2%), recall (95.8%) f1-score (95.5%), memory utilization (80 MB) and execution time (0.7 sec) respectively. © 2023 IEEE.

关键词： Attribute-Based Encryption Data Preprocessing Healthcare IoT Public-Key Authenticated Searchable Encryption Query Processing Searchable Encryption

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SalineSync: A Saline Level Monitoring IoT System

SalineSync: A Saline Level Monitoring IoT System

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Kanade, Dnyaneshwar Paldewar, Ameya Paliwal, Sanket Palwe, Amol Panchal, Soham Patil, Bhavesh Vishwakarma Institute of Technology Department of Electronics & Telecommunication Engineering Pune India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350313987

In the realm of he althc are, precise intravenous therapy (IV) involving saline solutions is paramount for patient well-being. Accurate monitoring of saline levels is essential to ensure uninterrupted treatment, as deviations can result in serious consequences. However, traditional manual monitoring methods are fraught with errors and delays, underscoring the need for a more efficient and reliable solution. This research work presents a novel saline level monitoring approach through the implementation of Internet of Things (IoT) technology. The proposed system leverages IoT to enhance the safety and efficiency of saline monitoring in healthcare settings. By automating the monitoring process, it reduces the likelihood of medication errors and ensures patients receive the correct dosage. Furthermore, it enhances the operational efficiency of healthcare providers, freeing them from the time-consuming task of manual monitoring. This research study analyzes the technical aspects of the proposed system, encompassing IoT design, sensor selection and placement, data trans mission, and platform development. It also underscores the system's scalability, making it adaptable to various healthcare facility sizes and types. By deploying IoT technology, the proposed system promises improvements in terms of accuracy, efficiency, and patient safety, along with substantial cost reduction. This study offers comprehensive insights into the system's development and implementation, showcasing its potential to revolutionize heal thcare delivery and the benefits it can provide for the medical domain. © 2023 IEEE.

关键词： Healthcare Internet of Things Monitoring Patient Safety Smart System

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Advanced OptiDLCardioNet-Based Cardiac Arrhythmia Detection Model from ECG Signals 5

Advanced OptiDLCardioNet-Based Cardiac Arrhythmia Detection ...

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

作者： Muthukumar, B. Kartheesan, L. Pasupathy, Vijayalakshmi Surendran, R. Rajalakshmi Engineering College Department of Information Technology Chennai India Vel Tech Rangarajan Dr.Sangunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331530013

Heart disease is regarded as one of the most significant health problems dealing by people today. Approximately, cardiovascular disease (CVD) affects around 50 million people. Electrocardiogram (ECG) signals are more vital for identifying and keeping track of individuals with various CVDs. In order to detect different types of arrhythmias, this papers proposed a novel optimization driven deep learning model for cardiac arrhythmia detection, termed as OptiDLCardioNet. To improve and smoothen the ECG signal, a cascaded wavelet augmented Kalman (CWAK) filtering approach is first applied. Next, an Adaptive Position aware Black-winged SqueezeNet (APBWSqueezeNet) model is used for feature extraction. In order to classify the signals for arrhythmia disease diagnosis, the extracted features are input into an Enhanced Dilated Height-Width Axial Attention Convolutional Network (EDilBW-HWAACNet). Moreover, the hyper-parameters of the EDilBW-HWAACNet are adjusted through the application of the Improved Walrus Optimization Algorithm (IWOA). The MIT-BIH arrhythmia database is used to validate the performance of OptiDLCardioNet model. Rendering to the experimental results, the OptiDLCardioNet model is able to achieve high classification accuracy of 99.82%, which is superior to existing methods with fewer significant features. © 2024 IEEE.

关键词： Deep learning

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