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An Efficient Approach For Pathogens Detection in Bacterial Pneumonia

An Efficient Approach For Pathogens Detection in Bacterial P...

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international conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： Afeefa Rafeeque Rashid Ali Interdisciplinary Centre for Artificial Intelligence Aligarh Muslim University Aligarh India Department of Computer Engineering Interdisciplinary Centre for Artificial Intelligence Aligarh Muslim University Aligarh India

ISBN: (纸本)9781665476485

Pathogens detection in Bacterial Pneumonia can be understood as a classification of types of Bacterial Pneumonia. the motive is to provide the pathogen-directed antibiotics at the right time through the automation of detecting the pathogens in Bacterial Pneumonia with Artificial Intelligence. this study has combined the pre-trained model with machine learning classifiers for binary classification. the pre-trained model VGG16 is fine-tuned and then the features are extracted from this model. For the classification purpose, three machine learning classifiers (SVM, Random Forest, and Bagging) are used to evaluate the performance of the model on two metrics Testing Accuracy and Validation Accuracy. To generalize the model we have also used one of the most widely used K-Fold cross-validation techniques. A comparison is also made between the existing methodology and ours on the same dataset used in this study. Our methodology performs better by achieving the highest testing accuracy of 90.8% with Bagging as a Classifier. VGG16 achieved the lowest testing accuracy of 82.63% whereas VGG16 with Support Vector machine achieved 89.65% of testing accuracy and with Random Forest 88.77%.

关键词： Support vector machines Pathogens Microorganisms Pulmonary diseases machine learning signal processing Feature extraction

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Range Estimation of Radar Targets Using a Temporal Convolutional Network

Range Estimation of Radar Targets Using a Temporal Convoluti...

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international conference on signal processing and Information Security (ICSPIS)

作者： David Chuong Zachary Poole Webert Montlouis ECE Department Johns Hopkins University Baltimore MD USA

ISBN: (纸本)9781665492669

this paper investigates the radar range estimation problem. Using classical signal processing techniques, current radar systems are generally able to accurately detect targets and estimate their ranges. However, radar systems that need to directly sample signals at RF are too computationally intensive to obtain an accurate range estimate in the required time window. In addition, as the amount of data increases the number of computations will also increase. this paper will demonstrate that by using a trained neural network to perform sequence-to-sequence classification on a radar signal, it is possible to detect targets that are within the radar system’s field of vision and estimate their ranges with an overall accuracy of 99% in a fraction of time as compared to conventional radar signal processing.

关键词： Radio frequency Deep learning Convolution machine vision Neural networks Radar detection Estimation

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TimeStacking: An Improved Ensemble learning Method for Continuous Time Series Classification 7th

TimeStacking: An Improved Ensemble Learning Method for Conti...

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18th IFIP WG 5.1 international conference on Product Lifecycle Management (PLM)

作者： Alves Ribeiro, Victor Henrique Reynoso-Meza, Gilberto Pontificia Univ Catolica Parana PUCPR Ind & Syst Engn Grad Program PPGEPS Curitiba Parana Brazil Hilab Curitiba Parana Brazil

ISBN: (纸本)9783030943998;9783030943981

machine learning has gained great attention for solving time series classification problems. However, usual machine learning algorithms rely on learning from tabular data, and additional signal processing and data manipulation are necessary. Ensemble learning algorithms are famous for improving the performance in machine learning tasks by combining multiple predictors, but the usual techniques only take into account a single prediction from each base model. To improve the performance in time series classification tasks, this work proposes TimeStacking, a novel algorithm based on the famous ensemble learning technique stacked generalization (Stacking). Such an algorithm also takes into account the previous predictions of the base models to improve continuous time series classification tasks. Experiments are performed on a real-world dataset for drinking water quality monitoring, where TimeStacking achieves superior performance in comparison to Stacking and two other ensemble learning models, with over 10% improvement in terms of range-based F-1 score and over 30% in terms of range-based precision. therefore, results show the effectiveness of TimeStacking for solving continuous time series classification problems.

关键词： machine learning Ensemble learning Blending Time series classification Drinking water quality

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Classification of Bearing Faults using Discrete Wavelet Packet Analysis and Support Vector machine

Classification of Bearing Faults using Discrete Wavelet Pack...

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international conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： Afroz Ahmed Saad Sidra Khanam Dept. of Mechanical Engineering Aligarh Muslim University Aligarh India

ISBN: (纸本)9781665476485

the automation of condition monitoring strategies of industrial sectors is on the rise with the current industrial revolution. the automatic detection and classification of bearing faults using machine learning is also one of the application domains. A lot has been investigated into the use of different classifiers for bearing fault classification and of them, Support Vector machine (SVM) has emerged out as a simple and confident classifier. Sufficient studies are available on selecting the features from raw vibration data and then training them using SVM; however, there is a dearth in investing the role of denoised signal for feature extraction. this work presents a comparative study of extracting the features from raw signal and signal processed through Discrete Wavelet Packet Analysis (DWPA). For this the opensource data from Case Western Reserve University has been used and the compressed and denoised signals are obtained using Symlet5 wavelet. the features obtained from raw, compressed and denoised signals are trained using different SVM classifier and their classification accuracies are compared. the classification accuracy is enhanced by using the denoised signal for fault investigation.

关键词： Support vector machines Vibrations Training Support vector machine classification signal processing Feature extraction Wavelet analysis

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An IoT machine learning Model-Based Real-Time Diagnostic and Monitoring System 4th

An IoT Machine Learning Model-Based Real-Time Diagnostic and...

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4th international conference on machine Intelligence and signal processing, MISP 2022

作者： Awotunde, Joseph Bamidele Misra, Sanjay Ajagbe, Sunday Adeola Ayo, Femi Emmanuel Gurjar, Ramchandra Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Computer Science and Communication Ostfold University College Halden Norway Department of Computer Engineering Ladoke Akintola University of Technology Ogbomosho Nigeria Department of Mathematical Sciences Olabisi Onabanjo University Ago-Iwoye Ogun State Nigeria Shri Govindram Seksaria Institute of Technology and Science Indore India

ISBN: (纸本)9789819900848

Internet of things (IoT) and machine learning (ML) have recently changed the paradigm in the area of applications in health-care systems with significant results. the ML-based models enabled with IoT-based systems have greatly expedited the medical diagnosis, real-time monitoring, and treatment in the health-care system, thus reducing the burden of health-care processes and the cost of taking care of patients by caregivers and physicians. therefore, in this paper, a framework is proposed based on an ML model-enabled IoT-based system for real-time diagnosis of various diseases using data collected from wearable devices and sensors attached within the body of patients. the captured data are stored using an integrated cloud database for further preprocessing and processes using ML-based models. ML model was used to select the most relevant features from the cloud database and thus use the preprocessed data for the diagnosis of various diseases. the findings from the study show that the XGBoost performs reliably better when compared with other existing ML-based models with an accuracy of 95.7%, recall of 95.9%, precision of 96.0%, F1-score of 94.8%, and Receiver Operating Characteristic Curve (ROC) of 94.3%. the results proposed a framework to show that the system can be widely used to diagnose various diseases and can be used in remote areas for better monitoring of patients. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Multiple Colour Detection of RGB Images Using machine learning Algorithm 5th

Multiple Colour Detection of RGB Images Using Machine Learni...

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5th international conference on Applied Informatics, ICAI 2022

作者： Awotunde, Joseph Bamidele Misra, Sanjay Obagwu, David Florez, Hector University of Ilorin Ilorin Nigeria Østfold University College Halden Norway Universidad Distrital Francisco Jose de Caldas Bogota Colombia

ISBN: (纸本)9783031196461

Colour detection is the act of identifying the name of any colour. Color detection is required for object recognition, and it is also utilized in a variety of picture editing and sketching programs. machine learning (ML) has been proof useful in this area, and lot of researches have been done. this has been utilized in fields of neural networks and digital image processing recently. RGB images multiple colour recognition are powerful tools for various images for images and sketches. there have been several suggested regression models that use crop image characteristics and image indices, however, they have not been properly tested for accuracy and adaptation effectiveness for multiple colors. therefore, this paper proposes K- Nearest Neighbour (KNN) classifier for efficient colour detection of RGB images. the KNN algorithm is a prominent ML technique and neural network classification technique. the KNN classifier is utilized to segregate distinct colors in the RGB images. the paper utilized colour histogram for feature extraction to find the features that most relevant pattern that define certain colours. the feature extraction further improved the efficacy and accuracy of KNN classifier’s in the classification of RGB images. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Color

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Sparse Representation of Sound Field Based on K-SVD

Sparse Representation of Sound Field Based on K-SVD

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IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Yuan Liu Wenqiang Liu Yongchang Li School of Mathematical Sciences and Key Laboratory of Architectural Acoustic Environment of Anhui Higher Education Institutes Anhui Jianzhu University Hefei China

Sparse representation has been applied to nearfield acoustic holography in order to provide an accurate reconstruction with a small number of sampling points. However, the performance of the application is based on the sparsity of the signal to be reconstructed. In this study, K-SVD dictionary learning method is used to construct a basis of the sound field and the sound field can be sparsely represented by the learned dictionary. the samples of K-SVD are the sound fields obtained by simulations rather than those measured in practice, by taking the advantage of the equivalent source method. Compared with other sparse bases used in the existing nearfield acoustic holography, the dictionary learned by K-SVD is more flexible and the prior of the sound field is not necessary. the numerical simulation results demonstrate the validity of advantage of the proposed method.

关键词： Dictionaries machine learning Holography signal processing Numerical simulation Acoustic measurements Acoustics

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Variational Autoencoder-Based Deep Neural Network for Coronary Heart Disease Risk Prediction 1

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17th international conference on Intelligent Information Hiding and Multimedia signal processing (IIH-MSP) / 14th international conference on Frontiers of Information Technology, Applications and Tools (FITAT)

作者： Amarbayasgalan, Tsatsral Park, Kwang Ho Davagdorj, Khishigsuren Ryu, Keun Ho theera-Umpon, Nipon Chungbuk Natl Univ Sch Elect & Comp Engn Database & Bioinformat Lab Cheongju 28644 South Korea Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City 700000 Vietnam Chungbuk Natl Univ Dept Comp Sci Coll Elect & Comp Engn Cheongju 28644 South Korea Chiang Mai Univ Inst Biomed Engn Chiang Mai 50200 Thailand

ISBN: (数字)9789811910579

ISBN: (纸本)9789811910579;9789811910562

Coronary heart disease (CHD) is one of the top causes of global mortality. Most patients cannot be diagnosed at the early stage because it does not give any symptoms for many years. If CHD gets worse, it will require advanced treatments, such as heart transplant and stent surgery. therefore, it is useful in preventing CHD by predicting high-risk people who will suffer from CHD. In this study, we have proposed a variational autoencoder (VAE)-based deep neural network (DNN) model for predicting CHD risk. We improved the performance of DNN model by enriching training dataset using VAE-based data generation. the proposed method has been compared with machine learning algorithms on Korea National Health and Nutrition Examination Survey (KNHANES) dataset. As a result, the proposed method outperformed the compared classifiers. the performance measurements include accuracy, precision, recall, F1-score, and AUC score which reached 0.851, 0.882, 0.809%, 0.844, and 0.851, respectively.

关键词： Coronary heart disease Deep neural network machine learning Variational autoencoder

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Metal and Metal Oxide Nanoparticle Image Analysis Using machine learning Algorithm 5th

Metal and Metal Oxide Nanoparticle Image Analysis Using Mach...

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5th EAI international conference on Big Data Innovation for Sustainable Cognitive Computing, BDCC 2022

作者： Bannigidad, Parashuram Potraj, Namita Gurubasavaraj, Prabhuodeyara Department of Computer Science Rani Channamma University Karnataka Belagavi India Department of Chemistry Rani Channamma University Karnataka Belagavi India

ISBN: (纸本)9783031283239

Nanomaterials are used in almost every field of engineering. Synthesis techniques and conditions greatly affect the properties of synthesized nanomaterials. Identifying the nanomaterial from FESEM and TEM images with bare eyes is an exceedingly impossible task. Digital image processing techniques play a vigorous part in identifying the size and structure, and classifying them precisely helps scientists and investigators to use them in numerous applications. the advantages of digital image processing techniques increase the precision of object recognition in computer vision and pattern recognition. the proposed technique extracts various textural features such as kurtosis, skewness, and entropy from boron, iron, and silver nanoparticle images. the classification is done by using PNN and K-NN classifiers. the K-NN classifier has an accuracy of 80.00% for boron, 86.67% for iron, and 93.33% for the silver nanoparticle images, and the PNN classifier has an accuracy of 86.67% for boron, 93.33% for iron, and 93.33% for silver nanoparticle images. Hence, based on the experimentation, the proposed study suggested that the PNN classification with texture features is the best classifier used to classify the boron, iron, and silver nanoparticle images as compared to the K-NN classifier. Further, the results also are established manually with chemical experts, which proves the exhaustiveness of the proposed method. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Synthesis (chemical)

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An Advanced and Ideal Method for Tumor Detection and Classification from MRI Image Using Gamma Distribution and Support Vector machine 5th

An Advanced and Ideal Method for Tumor Detection and Classif...

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5th international conference on Soft Computing and signal processing, ICSCSP 2022

作者： Aruna, S.K. Sambandam, Rakoth Kandan thaiyalnayaki, S. Vetriveeran, Divya —Kengeri Campus Karnataka Bangalore India Tamilnadu Chennai India

ISBN: (纸本)9789811986680

As indicated by a measurable report distributed by the registry of central brain tumor at United States (CBTRUS), roughly 59,550 individuals were recently diagnosed to have essential benign and essential harmful brain tumors in 2017. Besides, in excess of 91,000 individuals, in the United States alone, were living with an essential harmful cerebrum tumor and 367,000 were living with an essential kind brain tumor. the task of detecting the position of the tumor in the body of the patient is the starting point for a medical treatment in the diagnosis process. the main aim of this study is to design a computer system, which is able to detect the tumor presence in the digital images of the brain in the patient and to accurately define its borderline. In this proposed model, gamma distribution method is used for training, testing, and for the feature extraction process, while SVM, support vector machine is used for the classification process. Most of the algorithms find it difficult to segment the tumors that were present in the edges. But with the help of gamma distribution along with the use of edge analysis, it is easier to identify those tumor areas that are present in the edges, thus making it easier for the preprocessing process. Gamma distribution also provides us with high accuracy, and it can also point the exact location of the tumor than compared to other algorithms. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Tumors

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