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Detection of Diseases in Potato Leaves Using Transfer learning 1st

Detection of Diseases in Potato Leaves Using Transfer Learni...

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1st international Conference on Computational Intelligence in pattern recognition, CIPR 2019

作者： Dasgupta, Soumik Ranjan Rakshit, Somnath Mondal, Dhiman Kole, Dipak Kumar Jalpaiguri Government Engineering College Jalpaiguri735102 India Cyware Labs Bangalore560102 India

ISBN: (纸本)9789811390418

The problem of food shortage has grown rampant in the recent times in developing countries. In a tropical country like India, potato is one of the major staple food that is eaten throughout the year. Recently the production of potato is falling short due to various diseases like Early Blight and Late Blight which cause a huge loss of the cropped plants. This also leads to a major loss in the national economy as well. The emergence of deep learning has affected many fields of machine learning research. Since it is not required in deep learning to develop hand-crafted features, it has found widespread adoption in the scientific community. To tackle the need for a huge amount of data for deep learning, another heavily implemented technique is used, namely, transfer learning, to make the training process faster and more accurate with a relatively small dataset at hand. The performance of the model is demonstrated both quantitatively by computing the accuracy metric as well as visually. The model is lightweight and robust and thus can be added to an application in a handheld device like smartphone so that crop growers could spot the disease affected crops on the go and save them from getting ruined. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Online semi-supervised ensemble updates for fMRI data

Online semi-supervised ensemble updates for fMRI data

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1st IAPR-TC3 workshop on Partially Supervised learning, PSL 2011

作者： Plumpton, Catrin O. School of Computer Science Bangor University Dean Street LL57 1UT United Kingdom

ISBN: (纸本)9783642282577

Advances in Eelectroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have opened up the possibility for real time data classification. A small amount of labelled training data is usually available, followed by a large stream of unlabelled data. Noise and possible concept drift pose a further challenge. A fixed pre-trained classifier may not always work. One solution is to update the classifier in real-time. Since true labels are not available, the classifier is updated using the predicted label, a method called naive labelling. We propose to use classifier ensembles in order to counteract the adverse effect of 'run-away' classifiers, associated with naive labelling. A new ensemble method for naive labelling is proposed. The label taken to update each member-classifier is the ensemble prediction. We use an fMRI dataset to demonstrate the advantage of the proposed method over the fixed classifier and the single classifier updated through naive labelling. © 2012 Springer-Verlag.

关键词： Magnetic resonance imaging

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Cardiac Arrhythmia Diagnosis via Multichannel Independent Component Analysis: An Approach Towards a Better Health Care System 1st

Cardiac Arrhythmia Diagnosis via Multichannel Independent Co...

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1st international Conference on Emerging Technology Trends in Internet of Things and Computing (TIOTC)

作者： Sarfraz, Mohammad Khan, Mudassir Hasan Mohammed, Duraid Yahya Hussain, Mays Dheya Zidan, Khamis A. Aligarh Muslim Univ Ctr Interdisciplinary Biomed & Human Factors Engn Dept Elect Engn Aligarh 202002 Uttar Pradesh India AL Iraqia Univ Coll Engn Comp Engn Dept Baghdad Iraq

ISBN: (纸本)9783030972554;9783030972547

With the evolving lifestyle, many cardiac ailments are becoming more frequent, and it has become necessary to provide detailed surveillance of the heart's functioning to ensure healthy living. ECG signals provide details regarding the various forms of arrhythmias. However, owing to the complexities and non-linearity of ECG signals, it is impossible to examine these signals manually. Conventional approaches for specialist inspection of ECGs on paper or television are inadequate for ambulatory, long-term monitoring, and sports ECGs. Automated applications that use signal processing and pattern recognition would be extremely beneficial. Identification of arrhythmias from ECGs is an essential branch of biomedical signal processing and pattern recognition. Motion-induced artifacts are well-known to be a major source of misrecognition and misdiagnosis. On the other hand, the feature extraction method has a significant effect on the reliability and performance of ECG pattern recognition. This paper proposes new approaches and algorithms for pre-processing multi-channel ECG signals and neural networks for arrhythmia classification using independent component analysis (ICA) with two distinct goals: (1) to eliminate motion-induced or associated artifacts, and (2) to better select the features and allow more effective pattern recognition. When processing noisy ECG data with the MIT dataset, cross-validation reveals a major improvement. For noisy signals, classification sensitivity of 97.9% and positive predictivity of 98.1% was achieved in this work. A tenfold neural validation rule was used to achieve 99.3% accuracy in arrhythmia classification. The lower the signal-to-noise level, the more significant is the improvement. This proposed algorithm would be a valuable method for physicians in justifying their diagnosis. With its quick reaction time, the proposed algorithm can be easily integrated into an automated healthcare management system.

关键词： Health care Independent component analysis machine learning Arrhythmia computer-aided diagnosis Feature extraction Neural network

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Forecasting Network Intrusions from Security Logs Using LstMs 1st

Forecasting Network Intrusions from Security Logs Using LSTM...

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1st international workshop on Deployable machine learning for Security Defense, MLHat 2020, collocated with the 25th ACM SIGKDD Conference on Knowledge Discovery and data mining, KDD 2020

作者： Mueller, W. Graham Memory, Alex Bartrem, Kyle Leidos Inc. ArlingtonVA22203 United States

ISBN: (纸本)9783030596200

Computer network intrusions are of increasing concern to governments, companies, and other institutions. While technologies such as Intrusion Detection Systems (IDS) are growing in sophistication and adoption, early warning of intrusion attempts could help cybersecurity practitioners put defenses in place early and mitigate the effects of cyberattacks. It is widely known that cyberattacks progress through stages, which suggests that forecasting network intrusions may be possible if we are able to identify certain precursors. Despite this potential, forecasting intrusions remains a difficult problem. By leveraging the rapidly growing and widely varying data available from network monitoring and Security Information and Event Management (SIEM) systems, as well as recent advances in deep learning, we introduce a novel intrusion forecasting application. Using six months of data from a real, large organization, we demonstrate that this provides improved intrusion forecasting accuracy compared to existing methods. © 2020, Springer Nature Switzerland AG.

关键词： Deep learning

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Forex exchange using big data analytics 1

Forex exchange using big data analytics

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1st international Conference on Advances in Computational Science and Engineering, ICACSE 2020

作者： Sadhasivam, Jayakumar Arun, M. Deepa, R. Muthukumaran, V. Lokesh Kumar, R. Prasanna Kumar, R.B. Vellore Institute of Technology Vellore India School of Computing Kalasalingam Academy of Research and Education Tamilnadu 626128 India Telangana Hyderabad India Department of Mathematics School of Applied Science REVA University Bengaluru64 India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Vellore632 014 India Department of Computer Science REVA University Bengaluru64 India

Analysis and Prediction of forex has gained immense value in today's economy. The stock price prediction is a difficult process owing to the irregularities in stock prices. Every trader wants to know if the pattern has been repeated in past to know what the possible output of the current situation will be. The primary objective is to propose a methodology that will use a historical dataset and provide a more accurate prediction on stock price. In this paper, we will be using machine learning pattern recognition algorithm on forex tick dataset. The learned model then will produce pattern from the given dataset and on the pattern of increasing or decreasing, the buyer will initiate a buy or sell the stock respectively. We will use python coding to execute the algorithm in jupyter notebook. Matplot library will help us to perform graphing in the process and Numpy will be helpful in doing statistical analysis of data. © Published under licence by IOP Publishing Ltd.

关键词： Python

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5th IEEE/ACIS international Conference on Computer and Information Science and 1st IEEE/ACIS international workshop on Component-Based Software Engineering,Software Architecture and Reuse - Title

5th IEEE/ACIS International Conference on Computer and Infor...

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international Conference on Computer and Information Science (ACIS)

The following topics are dealt with: parallel and distributed computing; software metrics and project management; communication systems and networks; data mining; data warehousing; information management systems; Internet; mobile computing; wireless computing; software engineering; information engineering; management information systems; image processing and pattern recognition; computer architecture and software testing; artificial intelligence; intelligent agent technology; Web engineering

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Computational Science and Its Applications – ICCSA 2021 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Osvaldo Gervasi Beniamino Murgante Sanjay Misra Chiara Garau Ivan Blečić David Taniar Bernady O. Apduhan Ana Maria A.C. Rocha Eufemia Tarantino Carmelo Maria Torre

ISBN: (数字)9783030869601

ISBN: (纸本)9783030869595

The ten-volume set LNCS 12949 – 12958 constitutes the proceedings of the 21st international Conference on Computational Science and Its Applications, ICCSA 2021, which was held in Cagliari, Italy, during September 13 – 16, 2021. The event was organized in a hybrid mode due to the Covid-19 pandemic.;The 466 full and 18 short papers presented in these proceedings were carefully reviewed and selected from 1588 submissions. The books cover such topics as multicore architectures, mobile and wireless security, sensor networks, open source software, collaborative and social computing systems and tools, cryptography, human computer interaction, software design engineering, and others. Part II of the set follows two generaltracks: geometric modeling, graphics and visualization; advanced and emerging applications. Further sections include the proceedings of the workshops: international workshop on Advanced Transport Tools and Methods (A2TM 2021); international workshop on Advances in Artificial Intelligence learning Technologies: Blended learning, stEM, Computational Thinking and Coding (AAILT 2021); international workshop on Advancements in Applied machine-learning and data Analytics (AAMDA 2021). At the end of the book there is a block of short papers.;The chapter "Spatial justice models: an exploratory analysis on fair distribution of opportunities" is published open access under a CC BY license (Creative Commons Attribution 4.0 international License).

关键词： Computer Communication Networks Information Systems Applications (incl. Internet) Image Processing and Computer Vision machine learning Computer System Implementation pattern recognition

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Algorithmic approach to the recognition of cells in the sensorimotor cortex from microphotographs 1

Algorithmic approach to the recognition of cells in the sens...

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1st workshop on Information Systems for the Radiation Biology Tasks, ISRB 2020

作者： Kolesnikova, I.A. Budennaya, N.N. Severyukhin, Yu.S. Lalkovicova, M.G. Laboratory of Radiation Biology Joint Institute for Nuclear Research Dubna Russia Federal State-Funded Educational Institution of Higher Education of Moscow Region "Dubna University" Dubna Russia Slovak Academy of Sciences Institute of Experimental Physics Kosice Slovakia

The aim of the work is to study the effect of ionizing radiation on the central nervous system of laboratory animals. The Sector of Radiation Physiology of LRB JINR studies the effects of radiation on the cellular and organismal levels. Experimental animals are irradiated with protons or gamma rays in the JINR Medico-Technical Complex. The collection of biological material, its fixation, slicing, staining, microscopy and the creation of photographic images of the samples take two weeks or more, and the morphological analysis of the histological preparations takes approximately six months. Achievements in the application of artificial neural networks in the biomedical field indicate the possibility of automating the stage of analysis of histological preparations. The purpose of this work is to show the effectiveness of modern computer vision algorithms and machine learning methods for automating the stages of the morphological analysis of micropreparations of the brain of experimental animals after exposure to ionizing radiation. Thereby the speed of obtaining qualitative results increases, and the subjectivity of the approach to processing experimental data decreases. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： data handling

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A Multi-label learning Method to Detect Arrhythmia Based on 12-Lead ECGs 1st

A Multi-label Learning Method to Detect Arrhythmia Based on ...

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1st international workshop on machine learning and Medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th international Joint workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted stenting, CVII-stENT 2019, held in conjunction with 22nd international Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

作者： Zhu, Jinjing Xin, Kaifa Zhao, Qingqing Zhang, Yue Division of Information Science and Technology Tsinghua University Beijing China Renmin Hospital of Wuhan University Wuhan University Wuhan China

ISBN: (纸本)9783030333263

Cardiovascular disease (CVD) is one of the most serious diseases that harm human life and gives a huge burden to the health care system. Recent advances in deep learning have achieved great success in object detection, speech and image recognition. Although deep learning has been applied to the detection of arrhythmia, detection accuracy is limited because of three major issues: 1. Each ECG signal maybe contains more than one-label information;2. It is hard to classify ECG with different lengths;3. data imbalance problem is severe for arrhythmia detection. In this paper, we present a multi-label learning algorithm to address the class imbalance and detection on ECGs with different durations. We utilize Deep Convolutional Generative Adversarial Networks (DCGANs) and Wasserstein GAN-Gradient Penalty (WGAN-GP) to generate new positive samples and use two losses to balance the importance between positive samples and negative samples. Moreover, we construct a Squeeze and Excitation-ResNet (SE-ResNet) module for normal rhythm and arrhythmia detection. In order to solve the multi-label classification problem, we train nine different binary classifiers for each category and determine which types of rhythm the ECG signals belong to. Experimental results on The ECG Intelligence Challenge 2019 dataset demonstrate that our multi-label learning method achieves competitive performance in multi-label ECGs classification. © 2019, Springer Nature Switzerland AG.

关键词： Electrocardiograms

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data Collection and Wrangling Towards machine learning in LoD2+ Urban Models Generation 13th

Data Collection and Wrangling Towards Machine Learning in L...

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13th mining Humanistic data workshop, MHDW 2024, 9th workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 international workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Naserentin, Vasilis Spaias, George Kaimakamidis, Anestis Somanath, Sanjay Pantusheva, Mariya Mitkov, Radostin Dimara, Asimina Petrova-Antonova, Dessislava Anagnostopoulos, Christos-Nikolaos Logg, Anders Krinidis, stelios Gothenburg Sweden Thessaloniki Greece Sofia Bulgaria Kavala Greece Mytilene Greece

ISBN: (纸本)9783031632266

This paper presents a novel approach for streamlining data collection and wrangling processes to facilitate machine learning (ML) and Artificial Intelligence (AI) applications in generating Level of Detail 2+ (LoD2+) urban models. In the era of rapid urbanization, accurate and dynamic 3D city modeling has become indispensable for urban planning, disaster management, and Geographic Information Systems (GIS). The proposed methodology leverages the capabilities of public domain data sources like Google street View and OpenstreetMap to assist in creating comprehensive urban digital twins. By integrating diverse datasets within a unified data model, we aim to overcome the limitations posed by traditional urban modeling techniques. The Digital Twin Cities Centre (DTCC), hosted by Chalmers University of Technology, plays a pivotal role in this endeavor, providing an open-source platform for data fusion and urban design. The work presented is a milestone towards automating LoD2+ urban digital twins creation based on non-commercial software and data sources. © IFIP international Federation for Information Processing 2024.

关键词： Urban planning

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