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Prediction of Epileptic Seizures using Support Vector Machine and Regularization

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Prediction of Epileptic Seizures using Support Vector Machin...

IEEE Region 10 Symposium

作者： Shaikh Rezwan Rafid Ahmad Samee Mohammad Sayeed Zaziba Ahmed Nusayer Masud Siddique Mohammad Zavid Parvez Software Engineering and HCI Research Group Department of Computer Science and Engineering Brac University Bangladesh

ISBN: (数字)9781728173665

ISBN: (纸本)9781728173672

Epilepsy is a neurological disorder that causes abnormal behavior and recurrent seizures due to unusual brain activity. This study has attempted to predict seizures in epileptic patients through the process of feature extraction from EEG signals during preictal/ictal and interictal periods, classification and regularization. EEG signals from various parts of the brain from 10 epileptic patients are considered. Fast Fourier Transform (FFT) is used to determine the three features-the phase angle, the amplitude and the power spectral density of the signals. To classify the signals, these features are then used along with Support Vector Machine (SVM) as the classifier. Furthermore, regularization is used to make better predictions i.e. increase prediction accuracy and decrease the rate of false alarm. Finally, the proposed approach is tested on CHB-MIT Scalp EEG data set and it is able to predict epileptic seizures 25 minutes on average before the onset of the seizure with 100% accuracy and a low false-alarm rate of 0.46 per hour. This study intends to contribute to the development of better and advanced seizure predicting devices in the medical field.

关键词： Support vector machines Electroencephalography Feature extraction Brain modeling Predictive models Epilepsy Training

Comprehensive analysis of faults and diagnosis techniques in cascaded multi-level inverters

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Heliyon 2024年第21期10卷 e39901页

作者： Gatla, Ranjith Kumar Kumar, Devineni Gireesh Shashavali, Palthur Dsnm, Rao Kotb, Hossam Alkuhayli, Abdulaziz Ghadi, Yazeed Yasin Mbasso, Wulfran Fendzi Department of Electrical & Electronics Engineering Institute of Aeronautical Engineering Telangana Dundigal 500043 India Department of Electrical & Electronics Engineering B V Raju Institute of Technology Telangana Narsapur 502313 India Department of EEE S K University College of Engineering & Technology Andhra Pradesh Ananthapuramu 515003 India Department of EEE Gokaraju Rangaraju Institute of Engineering & Technology Telangana Bachupally 500090 India Department of Electrical Power and Machines Faculty of Engineering Alexandria University Alexandria 21544 Egypt Electrical Engineering Department College of Engineering King Saud University Riyadh 11421 Saudi Arabia Department of Computer Science and Software Engineering Al Ain University Abu Dhabi 15322 United Arab Emirates Laboratory of Technology and Applied Sciences University Institute of Technology University of Douala PO Box: 8698 Douala Cameroon

Reliability is a crucial factor to consider for multi-level inverters (MLIs) used in industrial applications. With the increasing number of power semiconductor devices, the potential for defects to significantly degrade the overall system is heightened. A highly effective fault-detection technique is required to minimize the impact of faults. This paper provides a comprehensive overview of the fundamental principles of multi-level inverters and the various sorts of faults that can occur in multi-level inverters. This study provides a comprehensive analysis of five-level cascaded H-bridge multilevel inverters (MLIs) under both normal and defective conditions. The paper outlines a fault-detection method that utilizes total harmonic distortion and a normalized output voltage factor. In addition, the paper discusses a fault-isolation strategy that relies on reducing amplitude modulation. This method leads to the development of a fault-tolerant inverter. The utilization of level-shifted pulse-width modulation (LSPWM) technology is employed for the purpose of switching operations. LSPWM is the most appropriate technique for MLIs that require a low amount of computational resources. The fault-diagnosis approach given is suitable for MLI-based drives, grid-connected operations, and other applications. This paper presents a comprehensive examination of the 5L-CMLI (5-Level Cascaded Multi-Level Inverter) under various fault scenarios in CMLI. Subsequently, various fault diagnosis approaches will be examined, including their advantages and disadvantages. The paper discusses several defects that can occur in the Insulated Gate Bipolar Transistor (IGBT) of a Current Mode Logic Inverter (CMLI), and also presents a design for a reliable fault diagnosis system. Furthermore, this analysis examines several fault detection strategies in CMLI, categorized according to open-loop and closed-loop dynamic systems fault classifications. © 2024 The Authors

关键词： Fault detection Multi-level inverters Open-loop and closed-loop systems Total harmonic distortion

Towards the statistical construction of hybrid development methods

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arXiv 2021年

作者： Tell, Paolo Klünder, Jil Küpper, Steffen Raffo, David MacDonell, Stephen Münch, Jürgen Pfahl, Dietmar Linssen, Oliver Kuhrmann, Marco Department of Computer Science IT University Copenhagen Denmark Software Engineering Group Leibniz University Hannover Hannover Germany Center for Digital Technologies Clausthal University of Technology Clausthal-Zellerfeld Germany Department of Computer Science School of Business Portland State University PortlandOR United States Department of Information Technology and Software Engineering Auckland University of Technology Auckland New Zealand Department of Computer Science Reutlingen University Reutlingen Germany Institute of Computer Science University of Tartu Tartu Estonia FOM Hochschule für Oekonomie & Management Essen Germany Software Engineering I University of Passau Passau Germany

Hardly any software development process is used as prescribed by authors or standards. Regardless of company size or industry sector, a majority of project teams and companies use hybrid development methods (short: hybrid methods) that combine different development methods and practices. Even though such hybrid methods are highly individualized, a common understanding of how to systematically construct synergetic practices is missing. In this article, we make a first step towards a statistical construction procedure for hybrid methods. Grounded in 1467 data points from a large-scale practitioner survey, we study the question: What are hybrid methods made of and how can they be systematically constructed? Our findings show that only eight methods and few practices build the core of modern software development. Using an 85% agreement level in the participants' selections, we provide examples illustrating how hybrid methods can be characterized by the practices they are made of. Furthermore, using this characterization, we develop an initial construction procedure, which allows for defining a method frame and enriching it incrementally to devise a hybrid method using ranked sets of practice. Copyright © 2021, The Authors. All rights reserved.

关键词： software design

Level of Trust and Privacy Management in 6G Intent-based Networks for Vertical Scenarios

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Level of Trust and Privacy Management in 6G Intent-based Net...

6G Networking (6GNet), International Conference on

作者： Jesú s A. Alonso-Lupez Luis Alberto Martí nez Herná ndez Sandra Pé rez Arteaga Ana Lucila Sandoval Orozco Luis Javier Garcí a Villalba Antonio Pastor Diego Ló pez Garcí a Department of Software Engineering and Artificial Intelligence (DISIA) Faculty of Computer Science and Engineering Office 431 Group of Analysis Security and Systems (GASS) Universidad Complutense de Madrid (UCM) Madrid Spain CTIO Unit Telef&#x00F3 nica I+D Ronda de la Comunicacion S/N Madrid Spain

Security is one of the main concerns when designing future 6G networks. Most of the components of service chains are software-based and their security must be ensured. In this work we propose a system that will check the level of trust of 6G services following 3 complementary approaches: attestation, generation of proofs of transit and usage of smart contracts whose execution will be published via Distributed Ledger Technology in order to enable their traceability. The service orchestration function will use reinforcement learning to formulate optimised service orchestration decisions. Services will be specified in a declarative model, following a privacy-aware flavour of intent-based networking. The proposed architecture will take into account privacy of every stakeholder: final users, service providers and infrastructure providers.

关键词： 6G mobile communication Privacy Distributed ledger Network topology Smart contracts Reinforcement learning Service-oriented architecture

Towards Mutual Trust-Based Matching For Federated Learning Client Selection

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Towards Mutual Trust-Based Matching For Federated Learning C...

International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Osama Wehbi Omar Abdel Wahab Azzam Mourad Hadi Otrok Hoda Alkhzaimi Mohsen Guizani Mohammad Bin Zayed University of Artificial Intelligence Abu Dhabi UAE Department of CSM Cyber Security Systems and Applied AI Research Center Lebanese American University Lebanon Department of Computer and Software Engineering Polytechnique Montréal Montreal Quebec Canada Division of Science New York University Abu Dhabi UAE Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE Division of Engineering New York University Abu Dhabi UAE

Federated Learning (FL) is a revolutionary privacy-preserving distributed learning framework that allows a small group of users to cooperatively build a machine-learning model using their own data locally. Smart cities are areas that can generate high volume and critical data, which has the potential to revolutionize federated learning. Nevertheless, it is highly challenging to select a trustworthy group of clients to collaborate in model training. The utilization of a random selection technique would pose many threats due to malicious clients’ targeted and untargeted attacks. Such vulnerability may cause attacks and poisoning in the produced model. To address this problem, we present a mutual trust client-server selection approach based on matching game theory and bootstrapping mechanisms for federated learning in smart cities. Our solution entails the creation of: (1) preference functions for federated servers and smart devices (i.e., IoT/IoV) that enables them to sort each other based on trust score, (2) light feedback-base technique that leverages the cooperation of multiple client devices to assign trust value to the newly connected federated server, and (3) intelligent matching algorithms consider trust preferences of both parties in their design. According to our simulation results, our technique outperforms the baseline selection approach VanillaFL in terms of increasing the trust level and hence the global accuracy of the federated learning model and optimizing the number of untrusted selected clients.

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Spin Hall Nano-Oscillator Empirical Electrical Model for Optimal On-chip Detector Design

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arXiv 2024年

作者： Fiorelli, Rafaella Rajabali, Mona Méndez-Romero, Roberto Kumar, Akash Litvinenko, Artem Serrano-Gotarredona, Teresa Moradi, Farshad Åkerman, Johan Linares-Barranco, Bernabé Peralías, Eduardo Instituto de Microelectrónica de Sevilla CSIC Universidad de Sevilla Sevilla41092 Spain NanOsc AB Kista16440 Sweden Applied Spintronics Group Department of Physics University of Gothenburg Gothenburg41296 Sweden Research Institute of Electrical Communication Center for Science and Innovation in Spintronics Tohoku University 2-1-1 Katahira Aoba-ku Sendai980-8577 Japan Electrical and Computer Engineering Department Aarhus University Aarhus N8200 Denmark

As nascent nonlinear oscillators, nano-constriction spin Hall nano-oscillators (SHNOs) represent a promising potential for integration into more complicated systems such as neural networks, magnetic field sensors, and radio frequency (RF) signal classification, their tunable high-frequency operating regime, easy synchronization, and CMOS compatibility can streamline the process. To implement SHNOs in any of these networks, the electrical features of a single device are needed before designing the signal detection CMOS circuitry. This study centers on presenting an empirical electrical model of the SHNO based on a comprehensive characterization of the output impedance of a single SHNO, and its available output power in the range of 2-10 GHz at various bias currents. © 2024, CC BY.

关键词： CMOS integrated circuits

Reliable extrapolation of deep neural operators informed by physics or sparse observations

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arXiv 2022年

作者： Zhu, Min Zhang, Handi Jiao, Anran Karniadakis, George Em Lu, Lu Department of Chemical and Biomolecular Engineering University of Pennsylvania PhiladelphiaPA19104 United States Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania PhiladelphiaPA19104 United States Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA19104 United States Division of Applied Mathematics Brown University ProvidenceRI02912 United States School of Engineering Brown University ProvidenceRI02912 United States

Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equations (PDEs) for real-time prediction, deep neural operators such as deep operator networks (DeepONets) provide a new simulation paradigm in science and engineering. Pure data-driven neural operators and deep learning models, in general, are usually limited to interpolation scenarios, where new predictions utilize inputs within the support of the training set. However, in the inference stage of real-world applications, the input may lie outside the support, i.e., extrapolation is required, which may result to large errors and unavoidable failure of deep learning models. Here, we address this challenge of extrapolation for deep neural operators. First, we systematically investigate the extrapolation behavior of DeepONets by quantifying the extrapolation complexity via the 2-Wasserstein distance between two function spaces and propose a new behavior of bias-variance trade-off for extrapolation with respect to model capacity. Subsequently, we develop a complete workflow, including extrapolation determination, and we propose five reliable learning methods that guarantee a safe prediction under extrapolation by requiring additional information—the governing PDEs of the system or sparse new observations. The proposed methods are based on either fine-tuning a pre-trained DeepONet or multifidelity learning. We demonstrate the effectiveness of the proposed framework for various types of parametric PDEs. Our systematic comparisons provide practical guidelines for selecting a proper extrapolation method depending on the available information, desired accuracy, and required inference speed. © 2022, CC BY-NC-ND.

关键词： Extrapolation

Probabilistic volumetric speckle suppression in OCT using deep learning

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arXiv 2023年

作者： Chintada, Bhaskara Rao Ruiz-Lopera, Sebastián Restrepo, René Bouma, Brett E. Villiger, Martin Uribe-Patarroyo, Néstor Wellman Center for Photomedicine Massachusetts General Hospital BostonMA02114 United States Harvard Medical School BostonMA02115 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02142 United States Applied Optics Group Universidad EAFIT Carrera 49 # 7 Sur-50 Medellín Colombia Institute of Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA02142 United States

We present a deep learning framework for volumetric speckle reduction in optical coherence tomography (OCT) based on a conditional generative adversarial network (cGAN) that leverages the volumetric nature of OCT data. In order to utilize the volumetric nature of OCT data, our network takes partial OCT volumes as input, resulting in artifact-free despeckled volumes that exhibit excellent speckle reduction and resolution preservation in all three dimensions. Furthermore, we address the ongoing challenge of generating ground truth data for supervised speckle suppression deep learning frameworks by using volumetric non-local means despeckling-TNode to generate training data. We show that, while TNode processing is computationally demanding, it serves as a convenient, accessible gold-standard source for training data;our cGAN replicates efficient suppression of speckle while preserving tissue structures with dimensions approaching the system resolution of non-local means despeckling while being two orders of magnitude faster than TNode. We demonstrate fast, effective, and high-quality despeckling of the proposed network in different tissue types acquired with three different OCT systems compared to existing deep learning methods. The open-source nature of our work facilitates re-training and deployment in any OCT system with an all-software implementation, working around the challenge of generating high-quality, speckle-free training data. © 2023, CC BY-NC-SA.

关键词： Optical tomography

An Exploration of Robotic Process Automation in all Spans of Corporate Considerations 7

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An Exploration of Robotic Process Automation in all Spans of...

7th International Conference on Advanced Computing and Communication Systems, ICACCS 2021

作者： Balakrishnan, S. Hameed, M.Syed Shahul Venkatesan, K. Aswin, G. Sri Krishna College of Engg. and Technology Dept. of Computer Science and Business Systems Tamilnadu Coimbatore India University of Technology and Applied Sciences - sur PO BOX: 484 PC 411 SUR Oman St. Josephs College of Engineering Department of Ece Full Stack Software Developer at Zealbots in Chadura Tech Tamilnadu Tiruchirapalli India

ISBN: (纸本)9781665405201

In advance, the change of digital transformation persistently operates the function of Robotic Process Automation (RPA) and draws out all forms of corporate considerations. The RPA raises a certain kind of business measure mechanization innovation that assists thought the software programming framework which enhances the system robots or man-made artificial intelligence (AI). While RPA is a famous subject in the corporate world and this holds scholarly exploration on research work which does not have a hypothetical and theoretical investigation over RPA. They portray the trait of RPA in a given direction which furnishes the concentration for additional exploration on advancement research which includes a software solution based on routine works, organized structured information and predetermined results. This helps in investigating the reports of emerging advantages in obtaining the efficiency of RPA with its cost and speed that decreases all the possible errors. © 2021 IEEE.

关键词： Robot programming