Machine learning and wearable devices for Phonocardiogram-based diagnosis

The heart sound signal, Phonocardiogram (PCG) is difficult to interpret even for experienced cardiologists. Interpretation are very subjective depending on the hearing ability of the physician. mHealth has been the adopted approach towards simplifying that and getting quick diagnosis using mobil devices. However, it has been challenging due to the required high quality of data, high computation load, and high-power consumption. The aim of this paper is to diagnose the heart condition based on Phonocardiogram analysis using Machine Learning techniques assuming limited processing power to be encapsulated later in a wearable device. The cardiovascular system is modelled in a transfer function to provide PCG signal recording as it would be recorded at the wrist. The signal is, then, decomposed using filter bank and the analysed using discriminant function. The results showed that PCG with a 19 dB Signal-to-Noise-Ratio can lead to 97.33% successful diagnosis. The same decomposed signal is then analysed using pattern recognition neural network, and the classification was 100% successful with 83.3% trust level.© CS & IT-CSCP 2019fi=vertaisarvioitu|en=peerReviewed

Phonocardiogram-based diagnosis using machine learning : parametric estimation with multivariant classification

The heart sound signal, Phonocardiogram (PCG) is difficult to interpret even for experienced cardiologists. Interpretation are very subjective depending on the hearing ability of the physician. mHealth has been the adopted approach towards quick diagnosis using mobile devices. However, it has been challenging due to the required high quality of data, high computation load, and high-power consumption. The aim of this paper is to diagnose the heart condition based on Phonocardiogram analysis using Machine Learning techniques assuming limited processing power to be encapsulated later in a mobile device. The cardiovascular system is modelled in a transfer function to provide PCG signal recording as it would be recorded at the wrist. The signal is, then, decomposed using filter bank and the analysed using discriminant function. The results showed that PCG with a 19 dB Signal-to-Noise-Ratio can lead to 97.33% successful diagnosis.fi=vertaisarvioitu|en=peerReviewed

Roadmap toward Smart Grids in Hydro and Thermal Power System : A Case study of the Ghanaian Power System

The evolution of Smart Grid flings fresh applications and opportunities to enhance the efficiency of power distribution networks. Network operators have the opportunity to make use of different sources of power. Communication between the network operators and the consumersis constantly permitted to allow optimization and balancing of energy usage. This paper seeks to evaluate the state of the Ghanaian Electric Distribution Network with respect to Smart Grid. We evaluate the performance of the traditional distribution network since its partial incorporation with the Smart Grid elements. The operations of the Supervisory Control and Data Acquisition, the Automated Meter Infrastructure and Circuit Breakers are specifically addressed. Road map to optimizing the distribution network in Ghana is presented. It is concluded that optimizing these key elements will transform the role of the distribution system and ensure a safe and reliable power network.©2020 IJAREEIE.fi=vertaisarvioimaton|en=nonPeerReviewed

Proposed algorithm for smart grid DDoS detection based on deep learning

The Smart Grid’s objective is to increase the electric grid’s dependability, security, and efficiency through extensive digital information and control technology deployment. As a result, it is necessary to apply real-time analysis and state estimation-based techniques to ensure efficient controls are implemented correctly. These systems are vulnerable to cyber-attacks, posing significant risks to the Smart Grid’s overall availability due to their reliance on communication technology. Therefore, effective intrusion detection algorithms are required to mitigate such attacks. In dealing with these uncertainties, we propose a hybrid deep learning algorithm that focuses on Distributed Denial of Service attacks on the communication infrastructure of the Smart Grid. The proposed algorithm is hybridized by the Convolutional Neural Network and the Gated Recurrent Unit algorithms. Simulations are done using a benchmark cyber security dataset of the Canadian Institute of Cybersecurity Intrusion Detection System. According to the simulation results, the proposed algorithm outperforms the current intrusion detection algorithms, with an overall accuracy rate of 99.7%.© 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Framework for Random Power Allocation of Wireless Sensor Networks in Fading Channels

In naturally deaf wireless sensor networks or generally when there is no feedback channel, the fixed-level transmit power of all nodes is the conventional and practical power allocation method. Using random power allocation for the broadcasting nodes has been recently proposed to overcome the limitations and problems of the fixed power allocation. However, the previous work discussed only the performance analysis when uniform power allocation is used for quasi-static channels. This paper gives a general framework to evaluate the performance (in terms of outage and average transmit power) of any truncated probability density function of the random allocated power. Furthermore, dynamic Rayleigh fading channel is considered during the performance analysis which gives more realistic results that the AWGN channels assumed in the previous work. The main objective of this paper is to evaluate the communication performance when general random power allocation is used. Furthermore, the truncated inverse exponential probability distribution of the random power allocation is proposed and compared with the fixed and the uniform power allocations. The performance analysis for the proposed schemes are given mathematically and evaluated via intensive simulations.© 2012 by authors and Scientific Research Publishing Inc. This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.fi=vertaisarvioitu|en=peerReviewed

Radio resource scheduling and smart antennas in cellular CDMA communication systems

This thesis discusses two important subjects in multi-user wireless communication systems, radio resource scheduler (RRS) and smart antenna. RRS optimizes the available resources among users to increase the capacity and enhance the system performance. The RRS optimization procedure is based on the network conditions (link gain, interference, …) and the required quality of service (QoS) of each user. The CDMA system capacity and performance can be greatly enhanced by reducing the interferences. One of the techniques to reduce the interferences is by exploiting the spatial structure of the interferences. This could be done by using smart antennas which are the second subject of this thesis. The joining procedures of the smart antennas and RRS are discussed as well. Multi-Objective optimization approach is proposed to solve the radio resource scheduler problems. New algorithms are derived namely the Multi-Objective Distributed Power Control (MODPC) algorithm, Multi-Objective Distributed Power and Rate Control (MODPRC) algorithm, and Maximum Throughput and Minimum Power Control (MTMPC) algorithm. Other modified versions of these algorithms have been obtained such as Multi-Objective Totally Distributed Power and Rate Control (MOTDPRC) algorithm, which can be used when only one-bit quantized Carrier to Interference Ratio (CIR) is available. Kalman filter is proposed as a second technique to solve the RRS problem. The motivation to use Kalman filter is the known fact that Kalman filter is the optimum linear tracking device on the basis of second order statistics. The RRS is formulated in state space form. Two different formulations are introduced. New simple and efficient estimation of the CIR is presented. The method is used to construct a novel power control algorithm called Estimated Step Power Control (ESPC) algorithm. The smart antenna concepts and algorithms are discussed. New adaptation algorithm is proposed. It is called General Minimum Variance Distortionless Response (GMVDR) algorithm. The joining of MIMO smart antennas and radio resource scheduler is investigated. Kalman filter is suggested as a simple algorithm to join smart antenna and multi-rate power control in a new way. The performance of the RRS of CDMA cellular communication systems in the presence of smart antenna is studied.reviewe

Machine learning for prognosis of oral cancer : What are the ethical challenges?

Background: Machine learning models have shown high performance, particularly in the diagnosis and prognosis of oral cancer. However, in actual everyday clinical practice, the diagnosis and prognosis using these models remain limited. This is due to the fact that these models have raised several ethical and morally laden dilemmas. Purpose: This study aims to provide a systematic stateof-the-art review of the ethical and social implications of machine learning models in oral cancer management. Methods: We searched the OvidMedline, PubMed, Scopus, Web of Science and Institute of Electrical and Electronics Engineers databases for articles examining the ethical issues of machine learning or artificial intelligence in medicine, healthcare or care providers. The Preferred Reporting Items for Systematic Review and Meta-Analysis was used in the searching and screening processes. Findings: A total of 33 studies examined the ethical challenges of machine learning models or artificial intelligence in medicine, healthcare or diagnostic analytics. Some ethical concerns were data privacy and confidentiality, peer disagreement (contradictory diagnostic or prognostic opinion between the model and the clinician), patient’s liberty to decide the type of treatment to follow may be violated, patients–clinicians’ relationship may change and the need for ethical and legal frameworks. Conclusion: Government, ethicists, clinicians, legal experts, patients’ representatives, data scientists and machine learning experts need to be involved in the development of internationally standardised and structured ethical review guidelines for the machine learning model to be beneficial in daily clinical practice.Copyright © 2020 for this paper by its authors. Use permitted under Creative CommonsLicense Attribution 4.0 International (CC BY 4.0).fi=vertaisarvioitu|en=peerReviewed

Cyber Security in Power Systems Using Meta-Heuristic and Deep Learning Algorithms

Supervisory Control and Data Acquisition system linked to Intelligent Electronic Devices over a communication network keeps an eye on smart grids’ performance and safety. The lack of algorithms protecting the power system communication protocols makes them vulnerable to cyberattacks, which can result in a hacker introducing false data into the operational network. This can result in delayed attack detection, which might harm the infrastructure, cause financial loss, or even result in fatalities. Similarly, attackers may be able to feed the system with fake information to hoax the operator and the algorithm into making bad decisions at crucial moments. This paper attempts to identify and classify such cyber-attacks by using numerous deep learning algorithms and optimizing the data features with a metaheuristic algorithm. We proposed a Restricted Boltzmann Machine-based nature-inspired artificial root foraging optimization algorithm. Using a publicly available dataset produced in Mississippi State University’s Oak Ridge National Laboratory, simulations are run on the Jupiter Notebook. Traditional supervised machine learning algorithms like Artificial Neural Networks, Convolutional Neural Networks, and Support Vector Machines are measured with the proposed algorithm to demonstrate the effectiveness of the algorithms. Simulations show that the proposed algorithm produced superior results, with an accuracy of 97.8% for binary classification, 95.6% for three-class classification, and 94.3% for multi-class classification. Thereby outperforming its counterpart algorithms in terms of accuracy, precision, recall, and f1 score.©2023 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

On the performance metrics for cyber-physical attack detection in smart grid

Supervisory Control and Data Acquisition (SCADA) systems play an important role in Smart Grid. Though the rapid evolution provides numerous advantages it is one of the most desired targets for malicious attackers. So far security measures deployed for SCADA systems detect cyber-attacks, however, the performance metrics are not up to the mark. In this paper, we have deployed an intrusion detection system to detect cyber-physical attacks in the SCADA system concatenating the Convolutional Neural Network and Gated Recurrent Unit as a collective approach. Extensive experiments are conducted using a benchmark dataset to validate the performance of the proposed intrusion detection model in a smart metering environment. Parameters such as accuracy, precision, and false-positive rate are compared with existing deep learning models. The proposed concatenated approach attains 98.84% detection accuracy which is much better than existing techniques.©The Author(s) 2022 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed