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Automatic Disease Classification of Paddy Leaf Images Using Deep learning Algorithms

Automatic Disease Classification of Paddy Leaf Images Using ...

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international conference on Signal Processing and Communication (ICSPC)

作者： Durgadevi Velusamy A Mounika D Ragulraj K Vigneshwaran Department of Computer Science and Engineering M.Kumarasamy College of Engineering Karur Tamilnadu India

Agriculture is considered one of the primary sectors in India. Especially rice is the primary stable food widely cultivated and consumed by 50% of the Indian population. Paddy crop is susceptible to several diseases like other crops that indirectly affect agricultural development and economic loss. To address this limitation, in this study, we have applied deep learning algorithms for the recognition and classification of diseases at an earlier stage. the paddy plant disease diagnosis is made by applying image processing techniques to identify the changes in leaves and its texture. We have used pre-processing techniques for segmentation, feature extraction and classification of disease. First, we have to perform data augmentation to improve the number of images in the dataset. Second, the segmentation of plant leaves are performed to identify the test infected region. then, we applied CNN algorithm to extract features from the data. After feature extraction, we trained different classification algorithms to recognize and classify the images into disease and non-disease images. the application of machine learning and deep learning algorithms helps in the precise location and identification of infectious leaves that help farmers to use automated methods for disease diagnosis.

关键词： Deep learning Support vector machines Image segmentation machine learning algorithms Signal processing algorithms Crops Feature extraction

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Case Study on the Use of the SafeML Approach in Training Autonomous Driving Vehicles 21st

Case Study on the Use of the SafeML Approach in Training Aut...

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21st international conference on Image Analysis and Processing (ICIAP)

作者： Bergler, Matthias Kolagari, Ramin Tavakoli Lundqvist, Kristina TH Nurnberg Kesslerpl 12 D-90489 Nurnberg Germany Malardalen Univ Sweden Kogskoleplan 1 S-72220 Vasteras Sweden

ISBN: (纸本)9783031064333;9783031064326

the development quality for the control software for autonomous vehicles is rapidly progressing, so that the control units in the field generally perform very reliably. Nevertheless, fatal misjudgments occasionally occur putting people at risk: such as the recent accident in which a Tesla vehicle in Autopilot mode rammed a police vehicle. Since the object recognition software which is a part of the control software is based on machine learning (ML) algorithms at its core, one can distinguish a training phase from a deployment phase of the software. In this paper we investigate to what extent the deployment phase has an impact on the robustness and reliability of the software;because just as traditional, software based on ML degrades with time. A widely known effect is the so-called concept drift: in this case, one finds that the deployment conditions in the field have changed and the software, based on the outdated training data, no longer responds adequately to the current field situation. In a previous research paper, we developed the SafeML approach with colleagues from the University of Hull, where datasets are compared for their statistical distance measures. In doing so, we detected that for simple, benchmark data, the statistical distance correlates with the classification accuracy in the field. the contribution of this paper is to analyze the applicability of the SafeML approach to complex, multidimensional data used in autonomous driving. In our analysis, we found that the SafeML approach can be used for this data as well. In practice, this would mean that a vehicle could constantly check itself and detect concept drift situation early.

关键词： Automotive Safety SafeML machine learning Autonomous driving

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Diabetes Classification Using Support Vector machine: Binary Classification Model 4

Diabetes Classification Using Support Vector Machine: Binary...

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4th international conference on Information and Communications Technology, ICOIACT 2021

作者： Rabiha, Suciana Ghadati Wibowo, Antoni Lukas Heryadi, Yaya Computer Science Department BINUS Graduate Program-Doctor of Computer Science. Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University 11480 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University 11480 Indonesia

ISBN: (纸本)9781665433945

One of the health problems that require special attention is diabetes, besides the growth of this disease infection is increasing in various circles ranging from children, adults, men, women and the elderly. So to detect diabetes at an early stage, machine learning algorithms are the right choice to help detect indications of diabetes. this paper presents experimental results related to the exploration of the use of a Super Vector machine (SVM) for data classification of Diabetes. the model used in this experiment is the Binary Classification Model. Based on the test results, the highest accuracy is obtained by using a total of 700 lines of data, which is 75,7143%. the Best estimated feasible point in Box Constraint is 272.29 with a KernelScale area of 72.627. © 2021 IEEE

关键词： Face recognition

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An Ensemble of Convolutional Neural Networks for Zero-Calibration ERP-Based BCIs 10

An Ensemble of Convolutional Neural Networks for Zero-Calibr...

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10th international Winter conference on Brain-Computer Interface (BCI)

作者： Dolzhikova, Irina Abibullaev, Berdakh Zollanvari, Amin Nazarbayev Univ Sch Engn & Digital Sci Elect & Comp Engn Dept Astana Kazakhstan Nazarbayev Univ Sch Engn & Digital Sci Robot & Mech Dept Astana Kazakhstan

ISBN: (纸本)9781665413374

the efforts to reduce user-specific training and calibration time of a Brain-Computer Interface (BCI) have been ramped up in the last several years. Such efforts have led to several studies that aimed at constructing subject-independent classifiers of mental intent;that is to say, BCIs that require no user-specific calibration process. this is a challenging problem due to major inter-subject variability of brain signals. In this work, we are primarily concerned with the same scenario;that is, constructing accurate subject-independent classifiers of mental intent. Given successful applications of convolutional neural network (CNN) in various areas including BCI, the utility of CNN in our work is not surprising. At the same time, the potential applications of ensemble learning in pattern recognition is not new. However, what is critical in ensemble-deep learning is how the ensemble per se is formed. the computational complexity involved in ensemble-deep learning is justified if the strategy used in forming the ensemble leads to a noticeable better classification performance. In this work, we propose multi-subject ensemble CNN (MS-En-CNN) classifier for zero-calibration BCI. this is an ensemble of CNN base classifiers that are trained using data collected from multiple subsets of training subjects. Our empirical results based on P300 visual-evoked potentials show that constructing MS-En-CNN leads to a significantly better subject-independent classification performance with respect to the average performance of the base CNN classifiers. Not only that, it is also shown that MS-En-CNN considerably improves (similar to 4% in our application) the average classification accuracy with respect to a single CNN that is trained based on pooled data from training subjects.

关键词： Multi-subject ensemble CNN Ensemble-deep learning Zero-calibration BCI

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MPTS-AFBP: Multi-pedestrian tracking and segmentation based on anchor-free detector 4

MPTS-AFBP: Multi-pedestrian tracking and segmentation based ...

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4th international conference on pattern recognition and Artificial Intelligence, PRAI 2021

作者： He, Xiaobing Cheng, Keyang Meng, Chunyun Jiangsu University School of Computer Science and Communication Engineering Zhenjiang China

ISBN: (纸本)9781665413220

In the last two years, a small amount of work has explored a new task called Multi-Object Tracking and Segmentation (MOTS), which aims to classify, locate, segment, and track all instances of a particular category in the whole video sequence. However, the existing MOTS algorithms have some problems, such as low segmentation accuracy and frequent object identity switching. In order to solve these problems, we propose a multi-pedestrian tracking and segmentation method based on anchor-free detector. Subsequently, in the segmentation branch, we fuse multiple information to refine the mask through blender module. In addition, in the tracking branch, we encode the position information of the object and combine it with appearance feature to generate association vector. In order to test the effectiveness of our method, we have carried out verification on the KITTI MOTS and MOTS Challenge dataset, and the experimental results prove the effectiveness of the method. © 2021 IEEE.

关键词： Deep learning

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Governmental Anti-Covid Measures Effectiveness Detection 27

Governmental Anti-Covid Measures Effectiveness Detection

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27th international conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Žid, Pavel Haindl, Michal Havlícek, Vojtech Institute of Information Theory and Automation Czech Academy of Sciences Pattern Recognition Department Pod Vodárenskou veží 4 Prague182 08 Czech Republic

We present a retrospective analysis of Czech anti-covid governmental measures' effectiveness for an unusually long three years of observation. Numerous Czech government restrictive measures illustrate this analysis applied to three years of COVID-19 data from the first three COVID-19 cases detected on 1st March 2020 till March 2023. It illustrates the course from the dramatic combat of unknown illness to resignation to country-wide measures and placing COVID-19 into a category of common nuisances. Our analysis uses the derived adaptive recursive Bayesian stochastic multidimensional Covid model-based prediction of nine essential publicly available COVID-19 data series. the COVID-19 model enables us to differentiate between effective measures and solely nuisance or antagonistic provisions and their correct or wrong timing. Our COVID model allows us to predict vital covid statistics such as the number of hospitalized, deaths, or symptomatic individuals, which can serve for daily control of anti-covid measures and the necessary precautions and formulate recommendations to control future pandemics. © 2023 the Authors. Published by Elsevier B.V. this is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Anti-pandemic measures COVID-19 machine learning method Prediction Recursive forecasting model

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A Survey on Open Set recognition 4

A Survey on Open Set Recognition

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4th IEEE international conference on Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Mahdavi, Atefeh Carvalho, Marco Florida Inst Technol Dept Engn & Sci Melbourne FL 32901 USA

ISBN: (纸本)9781665437363

Open Set recognition (OSR) is about dealing with unknown situations that were not learned by the models during training. In this paper, we provide a survey of existing works about OSR and distinguish their respective advantages and disadvantages to help out new researchers interested in the subject. the categorization of OSR models is provided along with an extensive summary of recent progress. Additionally, the relationships between OSR and its related tasks including multi-class classification and novelty detection are analyzed. It is concluded that OSR can appropriately deal with unknown instances in the real-world where capturing all possible classes in the training data is not practical. Lastly, some new directions for future research topics are suggested.

关键词： machine learning classification open set recognition multi-task learning risk of the unknown

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machine learning and Deep learning Analysis of PCG data

Machine Learning and Deep Learning Analysis of PCG Data

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Artificial Intelligence and Signal Processing (AISP), INDIA, international conference on

作者： Khyathi Devi Kotipalli Vyshnavi Gayam Hema Poojitha Chandu Radha Abburi Sibendu Samanta Department of CSE SRM University-AP India Department of ECE SRM University-AP India Dept of ECE & CSE SRM University-AP India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

Cardiovascular diseases are some of the most common diseases today. A new estimate from the World Heart Federation (WHF) states that the number of deaths from cardiovascular diseases (CVD) increased from 12.1 million in 1990 to 20.5 million in the year 2021. In recent years, the field of healthcare has witnessed significant advancements in technology and data analysis techniques. Congenital abnormalities, diseases caused by impaired heart rhythm, vascular occlusion, post-operation arrhythmias, heart attacks and irregularities in heart valves are some of the various cardiovascular diseases. Early recognition of them is very important for obtaining positive results in treatment. One such area of research that holds great promise is the classification of heart sounds using phonocardiography (PCG). Classification of heart sounds has become increasingly important in enhancing diagnostic precision and enhancing patient care. the integration of machine and deep learning into medical diagnostics has emerged as a transformative avenue. machine and Deep learning techniques offer the potential to automate and increase the accuracy of cardiac sound analysis providing healthcare professionals with rapid and constant diagnostics support. this research contributes to efficient cardiac diagnostics, aiding the timely detection of abnormalities and enhancing patient care. It aspires to learn about the value of heart sound classification, the utilization of phonocardiography, and the application of deep learning and machine learning methods for enhancing the accuracy of classification.

关键词： Deep learning Accuracy data analysis Heart beat Arrhythmia Cardiac arrest Signal processing Medical diagnosis Phonocardiography Heart valves

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Emotion recognition Based on Baseline-corrected Differential Entropy Features and Sparse Graph Regularized Extreme learning machine

Emotion Recognition Based on Baseline-corrected Differential...

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Computers and Artificial Intelligence Technologies (CAIT), international conference on

作者： Haoying Liu Xin Yan Yuzhu Guo Faculty of Information Techonology Beijing University of Technology Beijing China Sino-French Engineer School Beihang University Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China

In recent years, with the rapid development of brain science and information science, affective Brain Computer Interfaces (aBCI) have become a hot topic. EEG signals which record the electrical activity of neurons in the brain can provide direct evidence for emotional states. Compared with end-to-end deep learning methods, expert knowledge based emotion recognition methods, using hand-crafted features, are computationally cheaper and can have better generalization ability and interpretability. Due to the effort of balancing the energy spectrum of EEG signals, differential entropy features have been proved a powerful tool for emotion recognition. However, differential entropy of EEG signals still exhibits a decrease trend with increasing frequency, resulting in a low representation of the effect of high-frequency components in emotion recognition and do not perfectly solve the problem caused by the 1/f power law of EEG signals. In this paper, a decibel (dB) feature is extracted by introducing baseline normalization of EEG power spectral density (PSD) , which balances the effect of different rhythmic components better. On this basis, for the features are smoothed and a sparse graph regularized extreme learning machine (SGELM) is used to compare the dB feature with DE in emotion recognition. the proposed method is validated on DEAP data set, and the average classification accuracy increased by 11.33% over those of DE features with wild feature smoothing, and by 2.96% with high feature smoothness. the baseline normalized DE feature not only provide a new option for low-cost affective brain-computer interfaces can but also be used as an alternative input feature for deep learning models.

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Proceedings - 2021 4th international conference on Artificial Intelligence for Industries, AI4I 2021

Proceedings - 2021 4th International Conference on Artificia...

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4th international conference on Artificial Intelligence for Industries, AI4I 2021

ISBN: (纸本)9781665434102

the proceedings contain 21 papers. the topics discussed include: structure-borne and air-borne sound data for condition monitoring applications;enhancing feature selection in single shot robot learning by using multi-modal inputs;planning of curvature-optimal smooth paths for industrial robots using neural networks;traffic sign detection and recognition using deep learning;towards scalable abnormal behavior detection in automated surveillance;when to message: investigating user response prediction with machine learning for advertisement emails;forecasting of volatile assets using artificial swarm intelligence;and developing hierarchies for image classification model evaluation.

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