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Heart Disease Prediction Using Machine Learning Algorithms

Heart Disease Prediction Using Machine Learning Algorithms

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Innovations in Power and Advanced Computing Technologies (i-PACT)

作者： M. Priya A. Anitha M. K. Nallakaruppan J. Raju Raghavan R Divyansh Srivastava School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India School of Electrical Engineering Vellore Institute of Technology Vellore India

Heart disease is one of the destructive infections that an enormous populace of individuals all over the planet grieves. The prediction of heart disease is very much urged because of increasing death rates. The conventional method of prediction isn't adequate for such a disease. Evolving a clinical determination framework based on machine learning concepts gives more exact conclusions than the conventional way for the prediction of such illness. In this paper, we have used different machine learning algorithms to accurately foresee whether an individual has a heart illness or not. The concepts of machine learning like linear prediction KNN, SVM, and, ANN to predict the probability of heart disease in an individual are used. The medical and general details of the person like BMI, alcohol and tobacco consumption, etc. are used to make the prediction.

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Simultaneous Quantum information and Power Transfer-Based Green QKD Receiver Architectures

Simultaneous Quantum Information and Power Transfer-Based Gr...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Riyansh Chaturvedi Akhil Misra Sainath Bitragunta Ashuthosh Bhatia Kamlesh Tiwari Department of Electrical and Electronics Engineering BITS Pilani Pilani Rajasthan India Department of Computer Science and Information Systems BITS Pilani Pilani Rajasthan India

Considering the carbon footprint of rapidly evolving quantum systems and technologies, it is essential to develop energy efficient and sustainable next generation quantum communication systems. Simultaneous Lightwave information and Power Transfer (SLIPT) enables the transfer of power and information from a transmitter to a receiver through light waves in the infrared (IR) and visible portions of the electro-magnetic spectrum. In this work, the authors propose QKD-featured, SLIPT-enabled (QuIPT) quantum communication hybrid receiver architectures. Specifically, the authors present simplified green QKD receiver architectures integrating free space QKD and SLIPT. The proposed system is useful for developing QKD-featured autonomous quantum Internet of Light Things (QIoLT). The simplified architecture comprises two key modules: the QKD-decryption and decoding module and the energy harvesting subsystem module. Finally, the authors present additional remarks on extending the architectures to include radio frequency based energy harvesting (RFbEH) modules for superior performance.

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Fuzzy Logic and AI-Powered, SDR Relay for Secure and Efficient Cooperative Radio Communication

Fuzzy Logic and AI-Powered, SDR Relay for Secure and Efficie...

引用

International Conference on information Networking

作者： Srikar Bhamidipati Sainath Bitragunta Ashuthosh Bhatia Department of Electrical and Electronics Engineering BITS Pilani Pilani Rajasthan India Department of Computer Science and Information Systems BITS Pilani Pilani Rajasthan India

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

In this article, we develop a novel approach that leverages the capabilities of fuzzy logic and artificial intelligence (AI) to develop an intelligent, efficient cooperative RCN. Software defined radio (SDR) is flexible, scalable, and reconfigurable. Considering heterogeneous radio communication networks (RCNs), conventional relays do not perform well due to their limitations (security vulnerabilities in cooperative Internet-of-Things (IoT), inefficiencies in half-duplex relaying, etc.). We propose an AI-powered, fuzzy logic-based SDR relay to address these issues. These intelligent relays could be useful and outperform conventional relays due to their adaptability and reconfigurabilty, with added intelligence based on AI and fuzzy logic. The proposed next generation SDR relays offer significant advantages over traditional relays and have the potential to revolutionize the field of radio communication. Specifically, we analyze the decimation technique in SDR signal-to-interference plus noise ratio (SINR) resampler, Mamdani fuzzy logic controller, and use a machine learning (ML) model that uses RADIOML data set. Based on the simulation results, we show that applying fuzzy logic with an ML-enabled SDR relay could improve energy efficiency and reliability performance in advanced radio networks.

关键词： Fuzzy logic Simulation Machine learning Radio networks Security Reliability Relays

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An LLM-Based Digital Twin for Optimizing Human-in-the Loop systems

An LLM-Based Digital Twin for Optimizing Human-in-the Loop S...

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Foundation Models for Cyber-Physical systems & Internet of Things (FMSys), IEEE International Workshop on

作者： Hanqing Yang Marie Siew Carlee Joe-Wong Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9798350363456

ISBN: (纸本)9798350363463

The increasing prevalence of Cyber- Physical systems and the Internet of Things (CPS-loT) applications and Foundation Models are enabling new applications that leverage real-time control of the environment. For example, real-time control of Heating, Ventilation and Air-Conditioning (HVAC) systems can reduce its usage when not needed for the comfort of human occupants, hence reducing energy consumption. Collecting real-time feedback on human preferences in such human-in-the-Ioop (HITL) systems, however, is difficult in practice. We propose the use of large language models (LLMs) to deal with the challenges of dynamic environments and difficult-to-obtain data in CPS optimization. In this paper, we present a case study that employs LLM agents to mimic the behaviors and thermal preferences of various population groups (e.g. young families, the elderly) in a shopping mall. The aggregated thermal preferences are integrated into an agent-in-the-Ioop based reinforcement learning algorithm AitL-RL, which employs the LLM as a dynamic simulation of the physical environment to learn how to balance between energy savings and occupant comfort. Our results show that LLMs are capable of simulating complex population movements within large open spaces. Besides, AitL- RLdemonstrates superior performance compared to the popular existing policy of set point control, suggesting that adaptive and personalized decision-making is critical for efficient optimization in CPS-loT applications. Through this case study, we demonstrate the potential of integrating advanced Foundation Models like LLMs into CPS-loT to enhance system adaptability and efficiency. The project's code can be found on our GitHub repository.

关键词： Large language models Heuristic algorithms Sociology Reinforcement learning Ventilation Real-time systems Internet of Things

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Towards More Effective Insider Threat Countermeasures: A Survey of Approaches for Addressing Challenges and Limitations

Towards More Effective Insider Threat Countermeasures: A Sur...

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Annual IEEE systems Conference

作者： Omar Gonzales Shihong Huang KwangSoo Yang Electrical Engineering and Computer Science Florida Atlantic University Boca Raton FL USA Information Systems Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

This survey delves into challenges and limitations in addressing insider threats, utilizing both qualitative and quantitative questions to extract nuanced insights. It categorizes challenges based on characteristics and methodologies, conducting a comprehensive analysis across approaches to evaluate security control functions. The study systematically examines hurdles through targeted qualitative questions, providing a nuanced understanding of unique challenges. Additionally, quantitative questions assess each approach's adherence to security control functions. The findings underscore the complexity of addressing the human factor and emphasize the need for a unified approach integrating technical and behavioral factors. The paper highlights the urgency of implementing enhanced security measures and sets the stage for future research in insider threat mitigation.

关键词： Surveys Human factors Complexity theory Behavioral sciences Security

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Carbon-Aware Computing in a Network of Data Centers: A Hierarchical Game-Theoretic Approach

Carbon-Aware Computing in a Network of Data Centers: A Hiera...

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European Control Conference (ECC)

作者： Enno Breukelman Sophie Hall Giuseppe Belgioioso Florian Dörfler Division of Decision and Control Systems KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden Department of Electrical Engineering and Information Technology Automatic Control Laboratory ETH Zurich Zürich Switzerland

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainability. This paper focuses on the optimal allocation problem of batch compute loads with temporal and spatial flexibility across a global network of data centers. We propose a bilevel game-theoretic solution approach that captures the inherent hierarchical relationship between supervisory control objectives, such as carbon reduction and peak shaving, and operational objectives, such as priority-aware scheduling. Numerical simulations with real carbon intensity data demonstrate that the proposed approach success-fully reduces carbon emissions while simultaneously ensuring operational reliability and priority-aware scheduling.

关键词： Data centers Processor scheduling Computational modeling Carbon dioxide Games Supervisory control Resource management

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Designing Avatar System and Integrate to the Metaverse

Designing Avatar System and Integrate to the Metaverse

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International Technical Conference o Circuits/systems, computers and Communications (ITC-CSCC)

作者： Suebphong Noisri Pisut Wisessing Kunanont Srisupakwong Halim Budi Santoso Lunchakorn Wittisittikulkij Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Department of Computer Science Faculty of Science Kasetsart University Bangkok Thailand Department of Electrical Engineering Chulalongkorn University Bangkok Thailand Department of Information Systems Faculty of Information Technology Universitas Kristen Duta Wacana Yogyakarta Indonesia

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

Digital representations of users in virtual environments are known as avatars in the metaverse. They serve as the main vehicle for self-expression and identification in these digital places, enabling people to connect, socialize, and travel throughout the metaverse. Avatars' physical characteristics can be altered, such as their body type, skin tone, facial features, hairstyle, attire, and accessories. Design, development, and integration activities are usually combined for the avatar system to provide a smooth and interesting user experience. However, prior studies lack on how we should be able to customize the Avatar systems and integrate them into the Metaverse systems. This research tries to highlight and propose a method that might be able to deploy the avatar creation with a more straightforward user interface and apply it to the Metaverse environment. Our study provides a practical contribution to how to create a more straightforward avatar customization system and integrate it into the Metaverse Environment.

关键词： Metaverse Avatars Scalability Clothing systems architecture User interfaces User experience

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A Variable Step-Size Hybrid Adaptive Nonlinear Filter for Solar Radiation Prediction

A Variable Step-Size Hybrid Adaptive Nonlinear Filter for So...

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International Conference on systems and Informatics (ICSAI)

作者： Y. Xiao L. Ma K. Khorasani Dept. of Management & Information Systems Prefectural University of Hiroshima Hiroshima Japan Dept. of Electrical & Computer Engineering Concordia University Montreal Canada

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

In this paper, a new hybrid adaptive nonlinear filter (HANF) scheme with variable step sizes (VSS) is proposed for solar radiation prediction. Our methodology consists of a Volterra filter and a functional link artificial neural network (FLANN) filter. The Volterra filter with the first- and second-order kernels is included, that is capable of expressing both linearity and nonlinearity. The FLANN filter can handle the nonlinearity that may be expressed by higher-order exponential terms that the Volterra filter with a limited number of kernels is unable to deal with. The VSSs are introduced in the two filters to allow the HANF to enjoy desirable tracking capability such that the time-varying nonlinearity underlying the nonlinear phenomena can be detected and tracked. The proposed VSS-HANF is applied to a real hourly solar radiation time sequence to confirm its improved prediction performance as compared to its counterpart with fixed step sizes.

关键词： Accuracy Finite impulse response filters Adaptive filters Weather forecasting Linearity Nonlinear filters Artificial neural networks Solar radiation Kernel Informatics

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Digital AirComp-Assisted Federated Edge Learning with Adaptive Quantization

Digital AirComp-Assisted Federated Edge Learning with Adapti...

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IEEE International Conference on Human-Machine systems

作者： Ghazaleh Kianfar S. Jamal Seye Dmohammadi Jamshid Abouei Arash Mohammadi Konstantinos N. Plataniotis Concordia Institute of Information Systems Engineering (CIISE) Concordia University Montreal Canada Dept. of Electrical Engineering Yazd University Yazd Iran The Edward S. Rogers Sr. Dept. of Electrical & Computer Engineering University of Toronto Canada

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

Federated edge learning (FEEL) has been introduced for training machine learning models on distributed datasets for applications such as human monitoring. However, some challenges exist concerning the number of communication rounds and communication energy consumption involved in transmiting gradients during the training process. Moreover, over-the-air computation (AirComp) technology has gained attention recently, benefiting from superposition characteristic of wireless channels to compute functions over the air. While primarily designed for analog systems, there is potential for applying this technology in digital systems with embedded modulation schemes. This paper addresses these challenges by proposing a digital AirComp system for federated learning aggregation. The system employs a multi-bit quantization scheme to modulate gradients, adhering to a maximum transmission power constraint. An adaptive quantization scheme is also introduced, which considers the impact of quantization error and the error induced by additive white Gaussian noise. We derive closed-form expressions for pre-processing coefficients at devices and post-processing scaler at the access point (AP) to minimize the mean-squared error between high-precision and quantized qradients under the maximum transmission power constraint. Finally, the performance of the proposed scheme is evaluated in terms of achieved test accuracy, mean-squared error (MSE), and energy consumption, demonstrating its potential effectiveness compared to the benchmark schemes.

关键词： Training Performance evaluation Energy consumption Quantization (signal) Accuracy Federated learning Wireless networks

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Unveiling a New Vulnerability in Modern Power systems: Leveraging Publicly-Available LMPs for Crafting Cyber-Attacks

Unveiling a New Vulnerability in Modern Power Systems: Lever...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Mohammadmahdi Asghari Amir Ameli Mohsen Ghafouri Mohammad N. Uddin Department of Electrical and Computer Engineering Lakehead University Thunder Bay Canada Concordia Institute for Information Systems Engineering (CIISE) Concordia University Montreal Canada

Modern power systems are increasingly vulnerable to stealthy false data injection attacks (FDIAs) targeting state estimation. Executed without the operator’s knowledge, these attacks can lead to severe consequences, including economic losses, power outages, and full system blackouts. Previously, it was believed that executing destructive FDIAs requires full understanding of the operator’s response to such attacks, which demands in-depth knowledge of power system details, including generation prices and system constraints. Nevertheless, this paper reveals a noteworthy vulnerability wherein an attacker can employ publicly accessible historical locational marginal price (LMP) data and either all or a limited subset of phasor measurement unit (PMU) data to construct a predictive model. This model, utilizing a neural network, takes the loading profile of a section or the entire system as its input to forecast the LMPs. Such a model can be applied in designing high-damage FDIAs. Rigorous testing on the IEEE 30-, IEEE 57-, and IEEE 118- bus test systems demonstrates the model’s ability to predict the LMPs of the system with impressive accuracy and precision. This finding not only validates the model’s efficacy but also underscores the pressing need to address the newfound vulnerability of modern power systems against attackers utilizing public data.

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