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Epileptic EEG signal classification using convolutional neural networks based on optimum window length and FFT's length 20

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Epileptic EEG signal classification using convolutional neur...

8th International Conference on computer and Communications Management, ICCCM 2020

作者： Santoso, Irwan Budi Purnama, I. Ketut Eddy Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Engineering Universitas Islam Negeri Maulana Malik Ibrahim Malang Indonesia Department of Electrical Engineering Department of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9781450387668

This paper presents the method for the epilepsy classification based on electroencephalogram (EEG) signals. This method uses the spectrogram image of EEG signals and convolutional neural networks (CNN).The spectrogram image is obtained by mapping the spectrogram value of the results of the short-time Fourier Transform (STFT) to the RGB color map. The best spectrogram for CNN is determined based on the length of fast Fourier transform (FFT) and window length in the windowing technique. The proposed method is evaluated with the epileptic EEG signal datasets. The experimental results show CNN works optimally with spectrogram images from STFT on the window length of 128 and the length of FFT of 128. In these conditions, the performance of CNN in the classification of epilepsy is better than others and able to compete with existing methods. © 2020 ACM.

关键词： Convolutional neural networks

Deep Learning-Based Leaf Image Analysis for Tomato Plant Disease Detection and Classification

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Deep Learning-Based Leaf Image Analysis for Tomato Plant Dis...

IEEE International Conference on Image Processing

作者： Ammar Chouchane Abdelmalik Ouamanea El Ouanas Belabbaci Yassine Himeur Abbes Amira University Center of Barika Amdoukal Road Barika Algeria Laboratory of LI3C University of Biskra Biskra Algeria Laboratory of Medical Informatics (LIMED) Faculty of Technology University of Bejaia Bejaia Algeria College of Engineering and Information Technology University of Dubai Dubai UAE Department of Computer Science University of Sharjah UAE

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Tomato plant disease detection and classification, utilizing leaf images through deep learning, intersects the fields of plant pathology and agriculture. Deep learning has demonstrated significant potential in accurately identifying and classifying various plant diseases from leaf images. In this study, we introduce a hybrid system that combines a potent machine learning algorithm, Exponential Discriminant Analysis (EDA), with a transfer learning process leveraging recent and advanced deep networks, including ResNet50, Darknet53, DenseNet201, and EfficientNetB0. This system was evaluated using two challenging datasets: Taiwan and PlantVillage tomato leaf datasets. The experimental results underscore the high competitiveness of the proposed method, achieving mean accuracies of 98.29% and $\mathbf{9 8. 0 9 \%}$ on these datasets, respectively.

关键词： Deep learning Plant diseases Pathology Machine learning algorithms Image analysis Accuracy Plants (biology)

Weakly Supervised IDH-Status Glioma Classification from H&E-Stained Whole Slide Images

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Weakly Supervised IDH-Status Glioma Classification from H&E-...

IEEE International Symposium on Biomedical Imaging

作者： Shubham Innani Bhakti Baheti MacLean P. Nasrallah Spyridon Bakas Department of Pathology and Laboratory Medicine School of Medicine Division of Computational Pathology Indiana University Indianapolis IN USA Department of Pathology & Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Neurological Surgery School of Medicine Indiana University Indianapolis IN USA Department of Biostatistics & Health Data Science School of Medicine Indiana University Indianapolis IN USA Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University Indianapolis IN USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Gliomas are the most common malignant primary brain tumors in the Central Nervous System. Isocitrate dehydrogenase (IDH) mutation is a key driver of gliomagenesis in infiltrating gliomas and a diagnostic criterion with prognostic relevance. Determining IDH status from hematoxylin and eosin (H&E) stained Whole Slide Images (WSI) would be a valuable tool in precision oncology as it would expedite diagnosis and downstream patient treatment. We seek a computational approach to predict IDH status from digitized histopathology glioma sections (WSI). The proposed approach first applies a comprehensive curation step to remove artifactual content on each H&E-stained WSI, followed by a classification mechanism based on weakly supervised attention-based multiple instance learning. Quantitative evaluation on 1,534 digitized tissue sections from 799 cases from the publicly available TCGA-GBM and TCGA-LGG dataset revealed AUC of 0.92 and a balanced accuracy of 0.85 on a 10-fold cross-validation (10CV) schema. Histological prediction of IDH status represents an ideal prototype to interrogate WSI towards identifying clinically-relevant tumor biomarkers.

关键词： Accuracy Histopathology Prototypes Medical treatment Central nervous system Biomarkers Oncology

An Overview of Privacy Dimensions on Industrial Internet of Things (IIoT)

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arXiv 2023年

作者： Demertzi, Vasiliki Demertzis, Stavros Demertzis, Konstantinos Computer Science Department School of Science International Hellenic University Kavala Campus Kavala65404 Greece School of Spatial Planning and Development Faculty of Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece School of Science & Technology Informatics Studies Hellenic Open University Greece

Thanks to rapid technological developments, new innovative solutions and practical applications of the Industrial Internet of Things (IIoT) are being created, upgrading the structures of many industrial enterprises. IIoT brings the physical and digital environment together with minimal human intervention and profoundly transforms the economy and modern business. Data flowing through IIoT feed artificial intelligence tools, which perform intelligent functions such as performance tuning of interconnected machines, error correction, and preventive maintenance. However, IIoT deployments are vulnerable to sophisticated security threats at various levels of the connectivity and communications infrastructure they incorporate. The complex and often heterogeneous nature of chaotic IIoT infrastructures means that availability, confidentiality and integrity are difficult to guarantee. This can lead to potential mistrust of network operations, concerns about privacy breaches or loss of vital personal data and sensitive information of network end-users. This paper examines the privacy requirements of an IIoT ecosystem in industry standards. Specifically, it describes the industry privacy dimensions of the protection of natural persons through the processing of personal data by competent authorities for the prevention, investigation, detection or prosecution of criminal offences or the execution of criminal penalties. In addition, it presents an overview of the state-of-the-art methodologies and solutions for industrial privacy threats. Finally, it analyses the privacy requirements and suggestions for an ideal secure and private IIoT environment. © 2023, CC BY.

关键词： Internet of things

Robust Quantum Control in Closed and Open Systems: Theory and Practice

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arXiv 2023年

作者： Weidner, C.A. Reed, E.A. Monroe, J. Sheller, B. O’Neil, S. Maas, E. Jonckheere, E.A. Langbein, F.C. Schirmer, S.G. Quantum Engineering Technology Laboratories H. H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Department of Electrical and Computer Engineering Texas Tech University LubbockTX79409 United States Boeing Research & Technology Huntington BeachCA92647 United States Department of Computer Science Drake University 2507 University Ave Des MoinesIA50311 United States Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States School of Computer Science and Informatics Cardiff University CardiffCF24 4AG United Kingdom Swansea University SwanseaSA2 8PP United Kingdom

Robust control of quantum systems is an increasingly relevant field of study amidst the second quantum revolution, but there remains a gap between taming quantum physics and robust control in its modern analytical form that culminated in fundamental performance bounds. With certain exceptions such as quantum optical systems that can be modelled as linear stochastic differential equations, quantum systems are not amenable to linear, time-invariant, measurement-based robust control techniques, and thus novel gap-bridging techniques must be developed. This survey is written for control theorists to provide a review of the current state of quantum control and outline the challenges faced in trying to apply modern robust control to quantum systems. We present issues that arise when applying classical robust control theory to quantum systems, typical methods used by quantum physicists to explore such systems and their robustness, as well as a discussion of open problems to be addressed in the field. We focus on general, practical applications and recent work to enable control researchers to contribute to advancing this burgeoning field. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

The state-of-the-art review on resource allocation problem using artificial intelligence methods on various computing paradigms

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arXiv 2022年

作者： Joloudari, Javad Hassannataj Mojrian, Sanaz Saadatfar, Hamid Nodehi, Issa Fazl, Fatemeh Shirkharkolaie, Sahar Khanjani Alizadehsani, Roohallah Dipu Kabir, H.M. Tan, Ru-San Rajendra Acharya, U. Department of Computer Engineering Faculty of Engineering University of Birjand Birjand*** Iran Department of Information Technology Mazandaran University of Science and Technology Babol Iran Department of Computer Engineering Qom University of Technology Qom Iran Department of Electronic Engineering Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran Institute for Intelligent Systems Research and Innovation Deakin University GeelongVIC3216 Australia School of Science and Technology Singapore University of Social Sciences Singapore Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan

With the increasing growth of information through smart devices, increasing the quality level of human life requires various computational paradigms presentation including the Internet of Things, fog, and cloud. Between these three paradigms, the cloud computing paradigm as an emerging technology adds cloud layer services to the edge of the network so that resource allocation operations occur close to the end-user to reduce resource processing time and network traffic overhead. Hence, the resource allocation problem for its providers in terms of presenting a suitable platform, by using computational paradigms is considered a challenge. In general, resource allocation approaches are divided into two methods, including auction-based methods (goal, increase profits for service providers-increase user satisfaction and usability) and optimization-based methods (energy, cost, network exploitation, Runtime, reduction of time delay). In this paper, according to the latest scientific achievements, a comprehensive literature study (CLS) on artificial intelligence methods based on resource allocation optimization without considering auction-based methods in various computing environments are provided such as cloud computing, Vehicular Fog Computing, wireless, IoT, vehicular networks, 5G networks, vehicular cloud architecture, machine-to-machine communication (M2M), Train-to-Train(T2T) communication network, Peer-to-Peer (P2P) network. Since deep learning methods based on artificial intelligence are used as the most important methods in resource allocation problems;Therefore, in this paper, resource allocation approaches based on deep learning are also used in the mentioned computational environments such as deep reinforcement learning, Q-learning technique, reinforcement learning, online learning, and also Classical learning methods such as Bayesian learning, Cummins clustering, Markov decision process. © 2022, CC BY-SA.

关键词： Reinforcement learning

Enabling IoT Continuous Connectivity in Smart Spaces

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Enabling IoT Continuous Connectivity in Smart Spaces

International Symposium on Parallel and Distributed Computing

作者： Andreas Andreou Constandinos X. Mavromoustakis Jordi Mongay Batalla Ciprian Dobre Evangelos Markakis George Mastorakis Departement of Computer Science University of Nicosia and University of Nicosia Research Foundation Nicosia Cyprus Warsaw University of Technology Warsaw Poland Automatic Control and Computers University Politehnica of Bucharest National Institute for Research and Development in Informatics Bucharest Romania Department of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece Department of Management Science and Technology Hellenic Mediterranean University Agios Nikolaos Greece

Smart spaces are a rapidly emerging concept in technology. They result from the convergence of various novel technologies, such as the Internet of Things, Machine Learning and Artificial Intelligence, which allow for greater levels of automation and control within physical environments. The devices which are connected to the IoT network are equipped with sensors to acquire and exchange data. As a result, the IoT has transformed how we live, work, and play. However, the deployment in smart spaces is not always the best due to the issues arising from network node positioning. Therefore, we are investigating solutions to this problem with a novel approach which utilises Voronoi diagrams in conjunction with the algorithmic genetic technique. First, the initial positions of the IoT nodes will be determined by simulating a homogeneous Poisson point process in the smart space environment. Then, after dividing the area into the Voronoi cells, the genetic algorithm will optimise the position towards achieving full network coverage within the smart space. Experimental results prove the 100% network coverage within the specified area.

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MVC-VPR: Mutual Learning of Viewpoint Classification and Visual Place Recognition

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arXiv 2024年

作者： Gu, Qiwen Wang, Xufei Zhang, Fenglin Zhao, Junqiao Tao, Siyue Ye, Chen Feng, Tiantian Jiang, Changjun Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Shanghai Research Institute for Intelligent Autonomous System Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

Visual Place Recognition (VPR) aims to robustly identify locations by leveraging image retrieval based on descriptors encoded from environmental images. However, drastic appearance changes of images captured from different viewpoints at the same location pose incoherent supervision signals for descriptor learning, which severely hinder the performance of VPR. Previous work proposes classifying images based on manually defined rules or ground truth labels for viewpoints, followed by descriptor training based on the classification results. However, not all datasets have ground truth labels of viewpoints and manually defined rules may be suboptimal, leading to degraded descriptor performance. To address these challenges, we introduce the mutual learning of viewpoint self-classification and VPR. Starting from coarse classification based on geographical coordinates, we progress to finer classification of viewpoints using simple clustering techniques. The dataset is partitioned in an unsupervised manner while simultaneously training a descriptor extractor for place recognition. Experimental results show that this approach almost perfectly partitions the dataset based on viewpoints, thus achieving mutually reinforcing effects. Our method even excels state-of-the-art (SOTA) methods that partition datasets using ground truth labels. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

Application of machine learning techniques for predicting child mortality and identifying associated risk factors

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Application of machine learning techniques for predicting ch...

Conference on Information Communication Technology and Society (ICTAS)

作者： Elliot Mbunge Stephen G Fashoto Benhildah Muchemwa Richard C Millham Garikayi Chemhaka Maureen Nokuthula Sibiya Tafadzwa Dzinamarira Jolly Buwerimwe Department of Computer Science Faculty of Science and Engineering University of Eswatini formerly Swaziland Department of Information Faculty of Accounting and Informatics Durban University of Technology Umlazi South Africa Department of Statistics and Demography Faculty of Social Sciences University of Eswatini Eswatini Research Innovation and Engagement Mangosuthu University of Technology Umlazi South Africa ICAP Harare Zimbabwe

Despite continuous persistent efforts to enhance child health through, among other things, universal access to care, child mortality remains a significant public health concern on a global scale. Child mortality is attributed to several factors including birth asphyxia/trauma, demographic and socioeconomic factors, preterm birth and intrapartum-related complications, pneumonia, preventable and treatable diseases, congenital anomalies, poor access to quality healthcare, poor hygiene and nutrition, and sanitation among others. In many sub-Saharan African nations, including Zimbabwe, the use of machine learning techniques to predict child mortality is still in its infancy. Therefore, this study applied machine learning algorithms decision trees, random forest, logistic regression and XGBoost to develop child mortality predictive models that utilize nationally representative demographic and health survey data. The logistic regression classifier achieved an accuracy of 74 % , random forest 72%, Decision tree 72%, and XGBoost a high accuracy of 81%. All under-five predictive models achieved a precision of 95 %. However, logistic regression achieved a recall of 76%, random forest 74 % , Decision tree 74 % , and XGBoost 84%. Logistic Regression achieved F1-score of 84%, random forest 83%, Decision tree 83% and 89% for XGBoost. The XGBoost outperformed other under-five predictive models. Integrating such models into health information systems can significantly assist policymakers and healthcare professionals to improve the health status of children, access to quality care and most importantly, improve preventive measures, immunization programmes, policies, and decision-making to improve child health. Understanding the risk factors can assist in designing intervention programmes aimed at improve child health while reducing child mortality.

关键词： Pregnancy Pediatrics Machine learning algorithms Pulmonary diseases Predictive models Decision trees Indexes