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Security of Electronic Health Record using Attribute based Encryption on Cloud

Security of Electronic Health Record using Attribute based E...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： B. Murali Krishna I. Sapthami M. Srivani Ranjith Kumar Rupani Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of Computer Science and Engineering (Data Science) CVR College of Engineering Hyderabad Dept. of Computer Science and Engineering Koneru Lakshmaiah Education Foundation

Because of the likelihood of data breaches and the potential hazards of patient information, healthcare institutions face significant challenges in implementing cloud-based Electronic Health Record (EHR) systems. Current authorization models are based on a patientdriven approach to EHR system management, in which the patient’s agreement to allowing data access is prioritised. However, the patient has a considerable burden in ensuring the security of their healthcare record. Given the problems that healthcare providers confront, many employees are apprehensive to use cloud-based EHR systems. Data breaches and patient data compromise can result in serious legal and financial ramifications for healthcare organisations. To overcome these issues, healthcare organisations must implement more robust permission models that prioritise patient data protection and privacy. This necessitates a transition from the present patient-driven strategy to a more proactive one that prioritises data security and privacy across the EHR management process. By introducing more advanced permission models and robust security mechanisms, medical organisations can minimise the dangers associated with cloud-based EHR systems while also ensuring patient data privacy and security. Not only will this improve patient outcomes, but it will also shield healthcare organisations from legal and financial responsibilities.

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Harnessing ECU Data for Real-Time Fuel Consumption Forecasting

Harnessing ECU Data for Real-Time Fuel Consumption Forecasti...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： P. Kranthi Kumari V. Sravani Kumari M. Varshini B. Hari Krishna B. Prem Kumar V. Srinivas Department of Computer Science and Engineering Godavari Global University Rajamahendravaram Andhra Pradesh India Dept. of Computer Science and Engineering Godavari Institute of Engineering & Technology (Autonomous) Rajamahendravaram Andhra Pradesh India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

The Electronic Control Unit (ECU) is an integral part of a vehicle from which we can extract data which makes perfect sense regarding multiple aspects in the automotive sector. This is critical information for analysing driving behaviour. These capabilities are put together to work in the project, utilizing machine learning models to classify drivers based on their fuel efficiency and predict fuel consumption. The feature selection process enables to pinpoint the most noteworthy factors and utilize them to construct accurate fuel usage regression models. The proposed system implements algorithms like Random Forest Regression, AdaBoost Regression for prediction and K-Means Clustering for classification. With an in-depth review of driving profiles such as Sporty, Eco, Calm, Normal and Aggressive this project enables a broader comprehension of your complete drives and furthermore enabling a smarter fuel-efficient drive, which is noteworthy way for better vehicle execution and lower natural impact.

关键词： Reviews Biological system modeling Predictive models Feature extraction Real-time systems Fuels Forecasting Driver behavior Random forests Vehicles

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Risk Estimation in a Markov Cost Process: Lower and Upper Bounds 41

Risk Estimation in a Markov Cost Process: Lower and Upper Bo...

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41st International Conference on Machine Learning, ICML 2024

作者： Thoppe, Gugan Prashanth, L.A. Bhat, Sanjay P. Bengaluru India Robert Bosch Centre for Data Science and Artificial Intelligence IIT Madras Chennai India Dept. of Computer Science and Engineering Indian Institute of Technology Madras Chennai India TCS Research Hyderabad India

We tackle the problem of estimating risk measures of the infinite-horizon discounted cost of a Markov cost process. The risk measures we study include variance, Value-at-Risk (VaR), and Conditional Value-at-Risk (CVaR). First, we show that estimating any of these risk measures with ϵ-accuracy, either in expected or high-probability sense, requires at least Ω(1/ϵ2) samples. Then, using a truncation scheme, we derive an upper bound for the CVaR and variance estimation. This bound matches our lower bound up to logarithmic factors. Finally, we discuss an extension of our estimation scheme that covers more general risk measures satisfying a certain continuity criterion, such as spectral risk measures and utility-based shortfall risk. To the best of our knowledge, our work is the first to provide lower and upper bounds for estimating any risk measure beyond the mean within a Markovian setting. Our lower bounds also extend to the infinite-horizon discounted costs’ mean. Even in that case, our lower bound of Ω(1/ϵ2) improves upon the existing Ω(1/ϵ) bound (Metelli et al., 2023). Copyright 2024 by the author(s)

关键词： Cost estimating

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Real-Time Stress Level Monitoring Using IOT

Real-Time Stress Level Monitoring Using IOT

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Asha Patil, Nandini Preeti, B.M. Laxmi Sharnbasva University Dept. of Artificial Intelligence and Data Science Karnataka Kalaburagi India Sharnbasva University Dept. of Computer Science and Engineering Karnataka Kalaburagi India Sharnbasva University Dept. of Electrical and Electronics Engineering Karnataka Kalaburagi India

ISBN: (纸本)9798350315455

Stress is a prevalent and debilitating issue in contemporary society, contributing to a range of physical and mental health problems. As our lives become increasingly fastpaced and interconnected, addressing stress effectively has become imperative. This introduces a pioneering approach to tackle challenge through real-time stress level monitoring utilizing the Internet of Things (IoT). We propose a holistic system that merges IoT sensors, advanced data analytics, and machine learning techniques to provide continuous stress assessment and management solutions. The data is collected through the designed model then preprocessed, and essential features are extracted to construct a predictive model. The heart of the system is a machine learning model, which constantly updates itself based on user-specific stress patterns, yielding highly accurate real-time stress level estimations. Users are provided with actionable feedback and personalized recommendations, ranging from deep breathing exercises to lifestyle adjustments. Extensive experimentation demonstrates the system's robustness, with stress level predictions boasting a mean absolute error of under 5%. © 2023 IEEE.

关键词： data preprocessing feature extraction health monitoring Internet of Things (IoT) machine learning personalized recommendations physiological sensors real-time stress monitoring well-being

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SmartCitrus: An Efficient Deep Learning Approach for Real-Time Detection and Classification of Citrus Leaf Diseases

SmartCitrus: An Efficient Deep Learning Approach for Real-Ti...

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Advances in Computing,Communication, Electrical, and Smart Systems (iCACCESS), International Conference on

作者： Shaharear Hossain Emon Iftea Khairul Islam Tasfia Jahan Nahin Ahnaf Mahdin Ahmed Nabiha Tasnim Orchi Md Ashraful Alam Shakib Mahmud Dipto Dept. of Computer Science and Engineering Brac University Dhaka Bangladesh Dept. of Computer Science and Engineering CVIS Research Lab Brac University Dhaka Bangladesh Dept. of Computer Science and Engineering University of Liberal Arts Dhaka Bangladesh CVIS Research Lab Brac University Dhaka

Bangladesh is a prominent citrus exporter. Annually, the country has been exporting citrus fruits to over 60 countries. Distinguishing various diseases affecting citrus leaves requires a significant investment of time, effort, and specialized knowledge. Consequently, it is essential to create an innovative method for detecting citrus diseases. In this study, we have devised a valuable methodology by employing CNN models to identify diseases in citrus leaves. By employing a distinctive ensemble strategy, we successfully trained the model using varying numbers of classes in each stage. In reality, it allowed us to utilize suitable varieties of leaves for various ailments. Furthermore, it has enhanced the rate at which models learn during the later stages. In addition, it has reduced the level of model intricacy in comparison to frequently employed ensemble models. The identification of plant diseases in the present study involved the utilization of leaf photographs and algorithms for segmentation and feature extraction. Ultimately, we have successfully attained a 96 % accuracy rate for each class, signifying a substantial potential for mitigating production losses.

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Proof of Equation: A Novel Consensus Algorithm for Dynamic Spectrum Access 16

Proof of Equation: A Novel Consensus Algorithm for Dynamic S...

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16th IEEE AFRICON, AFRICON 2023

作者： Silva, Thiwanka Bamunuge, Madhushika Dissanayake, Dilusha Seneviratne, Chatura Gamage, Tharindu Liyanage, Madhusanka University of Ruhuna Faculty of Engineering Dept. of Electrical and Information Eng. Sri Lanka School of Computer Science University College of Dublin Ireland

ISBN: (纸本)9798350336214

The future of communication technology is moving from 5G to 6G with new innovations. Blockchain (BC) is a such immersive technology that significantly impacts the betterment of communication technology. BC-based spectrum-sharing solutions can be used in Dynamic Spectrum Access (DSA) systems to fulfill the need for secure and efficient communication. With the invention of cognitive radio networks, DSA became a popular topic for the scientific community. Spectrum misuse/violations can occur due to the rapid growth of spectrum sharing. As the system is open to malicious attacks, licensed spectrum owners must be identified and verified. However, the existing BC-based DSA solutions are more expensive, non-optimized, and lack spectrum misuse detection. This paper proposes a novel consensus algorithm called 'Proof of Equation' for spectrum misuse detection. The core of the proposed algorithm is a consensus score calculation based on a numerical equation with three parameters rather than using cryptographic calculations. The performance of the proposed algorithm is studied using Python simulations, and simulation results show that the proposed algorithm outperforms the Proof of Work (PoW) and Proof of Stake (PoS) consensus algorithms in terms of block production time. © 2023 IEEE.

关键词： Smart contract

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Intelligent Constellation Generation based on Autoencoder Communication System 2

Intelligent Constellation Generation based on Autoencoder Co...

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2nd IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2023

作者： Matsumoto, Kaisei Toma, Takao Oshiro, Shiho Wada, Tomohisa Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Area of Computer Science and Intelligent Systems University of the Ryukyus Dept. of Engineering Okinawa Japan

ISBN: (纸本)9798350305142

This paper discusses intelligent constellation generation based on autoencoder communication system. In previous studies, the amplitude was set to fluctuate between r=0.0 and 1.0. However, when checking the generated constellation, distortion was confirmed instead of the conventional symbol arrangement. Therefore, in this paper, it compares the case where the amplitude is constant, the case where the average amplitude within a Minibatch is 1, and the case where the average amplitude is 1 for Interval time. The communication standard used in this research is IEEE 802.11a, assuming wireless Local Area Network (LAN) specifications. The IEEE 802.11a standard has an Fast Fourier Transform (FFT) length of 64, a subcarrier number of 52, and Quadrature Phase Shift Keying (QPSK) and 16 Quadrature Amplitude Modulation (QAM), modulation methods. A guard interval of 800 ns is added and the symbol length is 4000 ns. First, a simulation was performed under the condition that the amplitude was kept constant. QPSK with 4 symbols, constant amplitude model is rounded more than previous research result. 16QAM with 16 symbols is arranged regularly like lined up on a line. Second, the simulation was performed under the condition that the average amplitude within the minibatch was set to 1. QPSK with 4 symbols, appears to rotate clockwise. 16QAM with 16 symbols has a more uniform symbol placement than previous research result. Third, a simulation was performed under the condition that the average amplitude within Interval time was set to 1. QPSK with 4 symbols, is the closest to square among QPSK output results so far. The direction is slightly tilted, but if it can be rotated a little more, it may be possible to reproduce the same symbol arrangement as before. 16QAM with 16 symbols, the symbol arrangement is biased as a whole. However, it can be seen that are arranged in line on the line, perhaps due to regularity. As future work, in addition to the conditions set this time, it will exa

关键词： Quadrature phase shift keying

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Sign Language Detection Using Action Recognition LSTM Deep Learning Model 2

Sign Language Detection Using Action Recognition LSTM Deep L...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Snehalatha, N. Mohana Kumar, S. Kavitha, H. Nisha, S. Gowda, Puttabore Krishna Prasad, Sj Jss Academy of Technical Education Dept. of Computer Science and Engineering Bangalore India Bangalore India Siddaganga Institute of Technology Dept. of Information Science Tumukur India Bangalore India Ramaiah Institute of Technology Dept. of Me Bangalore India Visvesvaraya College of Engineering Dept. of Ec University Bangalore India

ISBN: (纸本)9798350372892

This project aims to develop a system that can recognize sign language gestures in real-time using computer vision techniques. The system is designed to bridge the communication gap between individuals with different hearing abilities by enabling accurate and effective communication through sign language. To achieve this, an image datasets is created, consisting of labeled images of sign language gestures for alphabets, digits, or other signs. This datasets is used to train a deep neural network using transfer learning techniques. Transfer learning involves leveraging the knowledge gained from pre-trained models, such as VGG-16 and ResNet, to accelerate the training process and improve the accuracy of the model. The trained deep neural network is then deployed in a real- time setting where it processes video feeds or live camera feeds to recognize and interpret sign language gestures. The system is designed to be user-friendly, with an intuitive interface that can be easily used by individuals with hearing difficulties or those who do not understand sign language The accuracy of the system is evaluated using various performance metrics such as accuracy, precision, recall, and F1 score. The system is tested in different environments, including noisy and low-light settings, to ensure that it can recognize sign language gestures accurately. Overall, this work demonstrates the potential of computer vision and deep learning techniques to improve communication for individuals with hearing difficulties. It also highlights the importance of developing accessible technologies that can enable effective communication and improve the quality of life for individuals with disabilities © 2024 IEEE.

关键词： Deep neural networks

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A Unified Deep Learning Approach for Integrating Retinal Image Diagnosis and Surgical Tool Classification 1

A Unified Deep Learning Approach for Integrating Retinal Ima...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Jesica, J. Anitha, J. Sujitha Juliet, D. Belfin, R.V. Karunya Institute of Technology and Sciences Dept. of Data Science and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India University of North Carolina Dept. of Neurology Chapel Hill United States

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

Early and accurate diagnosis by using retinal image processing is critical for enabling optimized patient care. Existing techniques for the diagnosis in medical image processing often face limitations. This research study leverages the comparative analysis of advanced Convolutional Neural Networks (CNNs), including AlexNet, InceptionV3, Xception, and EfficientNetB3;out of which EfficientNetB3 gives the comparatively higher accuracy of 84%. This research work presents a novel deep learning-based approach for detecting diseases along with the integration of Lenscraft AI. Lenscraft AI helps to segment surgical masks and identify the surgical tools used in surgical recordings using YOLOv8. The developed tool and tracking method offers a higher precision value of 0.8863 and efficiency in identifying the surgical instruments and potentially improving surgical technique for the trainee surgeons and patient safety. © 2024 IEEE.

关键词： Convolutional neural networks

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Scalability in Autoencoder-based OFDM Communication System 2

Scalability in Autoencoder-based OFDM Communication System

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2nd IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2023

作者： Tsugawa, Seizan Toma, Takao Oshiro, Shiho Wada, Tomohisa University of the Ryukyus Computer Science and Intelligent Systems Program Okinawa Japan Magna Design Net Inc Okinawa Japan University of the Ryukyus Information Technology Center Okinawa Japan University of the Ryukyus Area of Computer Science and Intelligent Systems Dept. of Engineering Okinawa Japan

ISBN: (纸本)9798350305142

This paper proposed Scalability in Autoencoder-based Orthogonal Frequency Division Multiplexing(OFDM) communication system. In the previous research, only the comparison between IEEE802.11a and Autoencoder by the conventional OFDM communication system was performed, and it was proved that the communication system created by Autoencoder exceeded the performance of the conventional system. Therefore, in this paper, it uses IEEE802.11n and compare whether it can be improved by expanding the bandwidth and using ***802.11n standard has an FFT length of 128, a subcarrier number of 114 (108 for data), and modulation schemes of Quadrature Phase Shift Keying(QPSK), 16 Quadrature Amplitude Modulation (16QAM). The GI length is 800ns and the symbol length is 4000ns. In the simulation, a computer simulation was performed using a conventional OFDM communication system and a communication system generated by Autoencoder. Assuming that the simulation environment had an Signal-to-Noise Ratio (SNR) of 0 to 30 and an amplitude r of 0.0 to 1.0, the Symbol Error Rate (SER) status for each SNR was output. As a result of computer simulation, QPSK converged at SNR 27 at IEEE 802.11a, but was able to reduce SER overwhelmingly to SNR 12 at IEEE 802.11n. Also, in 16QAM the convergence at r=0.0 is the same as for SNR22 , but in IEEE 802.11a it does not converge after r=0.6, but in IEEE 802.11n it does not converge only at r=0.9 and r=1.0. As a future task, it will use IEEE802.11ac, which enables communication speeds several times faster than IEEE802.11n, examine whether it is possible to further improve accuracy. And it will continue our research to correspond MIMO communication. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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