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检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

共 777 条记录，以下是181-190 订阅

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Identification and Classification of Leaf Diseases Using Modified Contrast Limited AHE and Extreme learning machine 5

Identification and Classification of Leaf Diseases Using Mod...

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5th international conference on Inventive Computation Technologies (ICICT)

作者： Sunny, Shoby Gandhi, M. P. Indra Mother Teresa Womens Univ Dept Comp Sci Kodaikanal India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Agriculture has been a major revenue generator for the Indian economy. Around 60% of the Indian population is employed and dependent on agriculture. Biological factors such as diseases, insects, pests and weeds are one major reason that causes a low crop yield from plants during farming. this paper discusses how images of diseased leaves along with image processing can be used to identify and classify of various plant leaf diseases using Contrast Limited AHE and Modified Extreme learning machine algorithm. the initial part of the paper discuss how image enhancement is performed using modified Contrast Limited AHE. Enhancement is followed by segmentation which applies K-Means Clustering algorithm. GLCM, a statistical method of analytical texturing has been used to perform Texture feature extraction. Further, the paper discusses how different classifiers like SVM, Naive Bayes, KNN as well as Extreme learning machine are used to perform classification. the paper also introduces a Modified Extreme learning machine algorithm to classify the images in order to detect the presence of various diseases on plant leaves. Implementation of the proposed methodology shows improved results with respect to accuracy and reduced execution time in the detection process of Citrus Canker disease using Contrast Limited AHE and Modified Extreme learning machine algorithm.

关键词： Leaf Diseases Image processing CLAHE Extreme learning machine Image classification K-Means Clustering

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Alzheimer's Disease Detection Using Ensemble learning and Artificial Neural Networks 5th

Alzheimer's Disease Detection Using Ensemble Learning and Ar...

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5th international conference on Recent Trends in Image processing and Pattern Recognition (RTIP2R)

作者： Bandyopadhyay, Ahana Ghosh, Sourodip Bose, Moinak Singh, Arun Othmani, Alice Santosh, K. C. Appl AI Res Lab Vermillion SD 57069 USA Univ Paris Est LISSI UPEC F-94400 Vitry Sur Seine France Univ South Dakota Basic Biomed Sci Vermillion SD 57069 USA Univ South Dakota Dept Comp Sci Appl AI Res Lab Vermillion SD 57069 USA

ISBN: (纸本)9783031235986;9783031235993

this paper presents an ensemble method using machine learning classification algorithms and an artificial neural network-based scheme using the popular and widely used open access series of imaging studies (OASIS) dataset for Alzheimer's disease (AD) detection. the proposed work performs an in-depth feature examination and a training-test split in a 70 : 30 ratio on the dataset and applies 8 different ML algorithms. the AD detection outcome is obtained using two procedures, first by an ensemble approach applied to different machine learning algorithms, and secondly by using an artificial neural network (ANN). the use of ANN achieves an overall test accuracy of 0.9196 whereas two ensemble techniques, namely gradient boosting and voting classifier achieve an overall test accuracy of 0.857 and 0.8304. the precision and sensitivity scores demonstrate the superior detection performance of the ANN over the ensemble method on ML algorithms.

关键词： Alzheimer's disease detection machine learning ANN Ensemble learning

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machine learning Based Feature Extraction and Classification of Surface EMG signals in the Time Domain 5

Machine Learning Based Feature Extraction and Classification...

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5th Congreso Internacional en Inteligencia Ambiental, Ingenieria de Software y Salud ElectroWnica y Movil, AmITIC 2022 - 5th international Congress on Ambient Intelligence, Software Engineering and Electronic and Mobile Health, AmITIC 2022

作者： Losada, Cristian Pastrana, Brandot Rodriguez, Ruthber Ingenieria Mecatrónica Corporacion Universitaria del Huila Corhuila Neiva Colombia

ISBN: (数字)9781665485869

ISBN: (纸本)9781665485869

In this paper, the process of extracting and classifying six different EMG hand gestures using machine learning for future use in the control of a myoelectric hand prosthesis is presented. the process began with the extraction of the EMG data supplied by a patient suffering amputations in different parts of the forearm with an age range that ranges from 26 to 65 years, the recording of electromyographic signals was performed in two sessions in a non-invasive way. where the patients made the respective connected gestures using the Myo Armband bracelet and the data was stored in matrices. For the recognition of gestures based on EMG signals, we focus mainly on 3 areas: First, the pre-processing of the EMG signal where we improve the quality of the signal through filtering techniques to avoid noise in the signals obtained, the next area is the extraction of features where we obtained feature vectors of the EMG signal in the time domain, finally the recognition of the patterns in the signals using the following classifiers: MVS(Support Vector machine), RNA(Artificial Neural Red), KVC(K-Near Neighbors), Naive Bayes and Decision Trees. © 2022 IEEE.

关键词： Extraction

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Online Fake News Detection using Pre-trained Embeddings 5

Online Fake News Detection using Pre-trained Embeddings

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5th international conference on Multimedia, signal processing and Communication Technologies (IMPACT)

作者： Reshi, Junaid Ali Ali, Rashid Aligarh Muslim Univ Dept Comp Engn Aligarh Uttar Pradesh India Aligarh Muslim Univ Ctr Artificial Intelligence Dept Comp Engn & Interdisciplinary Aligarh Uttar Pradesh India

ISBN: (纸本)9781665476478

Fake news is a word, often heard. We often come across this term and its dangerous consequences with respect to society. To curb the menace of fake news is a big challenge in today's world. Many efforts have been made through different approaches to detect the fake news on online media. In this study, we propose a fake news detection system that utilizes pre-trained embeddings for the detection of fake news. We use a benchmark dataset of social media to carry our experiments. this study uses pre trained GloVe embedding based embedding matrix to generate the embeddings for the text as an input to our deep learning model. We also compare the performance of simple deep neural network based model with the LSTM based deep neural network on the same input embeddings. the results are indicative of good performance of the models to detect fake news.

关键词： Fake News Detection Word Embeddings Natural Language processing

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Research on Image Recognition System Based on Computer Artificial Intelligence Technology 4

Research on Image Recognition System Based on Computer Artif...

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4th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Xu, Zhihua School of Design and Art Jingdezhen Ceramic University Jingdezhen China

ISBN: (纸本)9798350359589

this paper uses mobile network mode to develop and model Python crawler based on Ubuntu16.04 and CUDA9.0. the visualization of this pattern was achieved using Tensor Board software. the hardware design of the system consists of three modules: linear array CCD sensor, DSP processor and signal processing circuit. through the reconstruction and learning of such models, the trained models are stored, so as to obtain higher precision image recognition. A method of classifying objects using Fourier description operators is proposed. the experimental results show that the accuracy of the image is 96.8% and the recognition time is 0.55 seconds. Motion tracking is based on the positioning and capture of the specific position of the human hand in each screen, and the captured frame rate is 28 frames per second. the system can quickly acquire images of 1024∗1500 pixels at a rate of 5 photos/second. through the static identification of posture and tracking of motion trajectory, the interaction between human and machine is better. © 2024 IEEE.

关键词： artificial intelligence digital signal processing fourier Image identification technology mobile network

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Federated learning for Lung Sound Analysis 5th

Federated Learning for Lung Sound Analysis

引用

5th international conference on Recent Trends in Image processing and Pattern Recognition (RTIP2R)

作者： Farjana, Afia Makkar, Aaisha Univ South Dakota Dept Comp Sci Appl Res Lab 414 E Clark St Vermillion SD 57069 USA

ISBN: (纸本)9783031235986;9783031235993

Despite the general success of employing artificial intelligence (AI) to help radiologists perform computer-aided patient diagnosis, creating good models with tiny datasets at different sites is still tough. Medical image analysis is crucial for the quick and precise detection of lung disease and helps clinicians treat patients effectively while averting more fatalities. this is why real-time medical data management is becoming essential in the healthcare industry, especially for systems that monitor patients from a distance. To overcome this challenge, we propose a different approach by utilizing a relatively new learning framework. Individual sites may jointly train a global model using this approach, referred to as federated learning. Without explicitly sharing datasets, federated learning combines training results from various sites to produce a global model. this makes sure that patient confidentiality is upheld across all sites. Additionally, the additional supervision gained from partner sites' results enhances the global model's overall detection capabilities. this study's primary goal is to determine how the federated learning (FL) approachmay offer amachine learning average model that is robust, accurate, and unbiased in detecting lung disorders. For this aim, we analyze 325 Lung Sound audio recordings collected from https://***/and, transform this audio signal into Melspectrograms. Once the labeling and preprocessing steps were carried out, a Convolutional Neural Network (CNN)(FederatedNet) model was used to classify the respiratory sounds into healthy and unhealthy. We achieved the result of almost 88% validation accuracy. Furthermore, this paper discusses the application of FL and its overview. Lastly, we discuss the main challenges to federated learning adoption and potential future benefits.

关键词： Federated learning Lung sound machine learning

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machine learning Based Problem Solving Approach in Green Computing 5

Machine Learning Based Problem Solving Approach in Green Com...

引用

5th international conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Bakre, Shashikant Shiralkar, Ashpana Shelar, Sachin V. Ingle, Suchita Aissms Ioit Electrical Engineering Department Pune India

ISBN: (纸本)9781665475242

the issues related to conventional generation of electricity arethe matter of concern for power sector today. these include diminishing stock of coal over a period of time, unavailability of good quality coal, non-sustainable issues, ash handling problems etc. Green energy is the alternative to overcome these problems. the green energy is sustainable, renewable and economical. In India, the existing ratio of conventional to non-conventional generation as on 30th June 2022 is 72:28%. It is required to further improve this ratio to the tune of 60:40%. the performance of the green energy systems can be optimized by AI ML based green computing. Under the umbrella of AI, several technologies have been emerged. these technologies are machine learning, deep learning, data analytics, robotics, neural networks, expert systems, fuzzy logic systems, natural language processing, genetic algorithms etc. the green computing can be made more effective through research as regards how to use these technologies. In this paper, a novice techniques of AI ML based green computing have been proposed. Python programming language is used as a back end programming tool. the proposed methods are simple, cost effective and feasible. © 2023 IEEE.

关键词： Data Analytics

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A Weak signal Detection Method Based on Spatial Spectrum-LSTM Neural Network 5

A Weak Signal Detection Method Based on Spatial Spectrum-LST...

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5th international conference on Information Communication and signal processing, ICICSP 2022

作者： Dong, Yaning Wu, Chuanzhang Zhu, Huizhu Xu, Feng Ren, Xin Electronics Research Institute Communication and Navigation System Department Avic Aeronautical Radio Shanghai China Harbin Engineering University Department of Information and Communication Engineering Harbin China

ISBN: (纸本)9781665485890

In this paper, we propose a weak signal detection method based on spatial spectrum-long short-term memory (LSTM) neural network to address the problem that the traditional blind detection method of weak signals is not effective in the condition of low signal-to-noise ratios. We firstly exploit the difference between the spatial spectrum transformed signal and noise to determine whether there is a weak signal. then, the LSTM neural network is used for feature learning to classify different samples. It can avoid the influence of the detection threshold on the detection performance of the system. Numerical results show that the detection performance of our method outperforms LSTM neural network, radial basis function neural network, traditional maximum-minimum eigenvalue, and energy detection methods. © 2022 IEEE.

关键词： Long short-term memory

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5th international conference on Recent Innovations in Computing , ICRIC 2022

5th International Conference on Recent Innovations in Comput...

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5th international conference on Recent Innovations in Computing , ICRIC 2022

ISBN: (纸本)9789819906000

the proceedings contain 66 papers. the special focus in this conference is on Recent Innovations in Computing. the topics include: the Importance of Selected LMS Logs Pre-processing Tasks on the Performance Metrics of Classification Models;analysis of Deep Pre-trained Models for Computer Vision Applications: Dog Breed Classification;an Efficient Recognition and Classification System for Paddy Leaf Disease Using Naïve Bayes with Optimization Algorithm;development of Decision-Making Prediction Model for Loan Eligibility Using Supervised machine learning;prediction of Osteoporosis Using Artificial Intelligence Techniques: A Review;analyzing Stock Market with machine learning Techniques;machine learning Techniques for Image Manipulation Detection: A Review and Analysis;improving Real-Time Intelligent Transportation Systems in Predicting Road Accident;Consumer Buying Behavior Analysis During COVID-19 Phase Using Statistical Data Mining and Multi-verse Stochastic Algorithm;clinical Named Entity Recognition Using U-Net Classification Model;addressing Role of Ambient Intelligence and Pervasive Computing in Today’s World;Improving the AODV Routing Protocol Against Network Layer Attacks Using AntNet Algorithm in VANETs;a Comparative Study of IoT-Based Automated Hydroponic Smart Farms: An Urban Farming Perspective;Smart Healthcare System Based on AIoT Emerging Technologies: A Brief Review;Intelligent Communication for Internet of things (IoRT);energy-Efficient Priority-Based Routing Model for Smart Health Care;An Internet of things-Based Mining Worker Safety Helmet Using ESP32 and Blynk App;Time Series-Based IDS for Detecting Botnet Attacks in IoT and Embedded Devices;swarm Intelligence-Based Energy-Efficient Framework in IoT;Performance Analysis of MADM Techniques in Cognitive Radio for Proximity Services;neural machine Translation from English to Marathi Using Various Techniques.

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A Study on machine learning Based Diabetic Retinopathy 5

A Study on Machine Learning Based Diabetic Retinopathy

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5th international conference on Inventive Computation Technologies (ICICT)

作者： Shreyas, M. D. Rani, Asha K. P. Gowrishankar, S. Dr Ambedkar Inst Technol Dept Comp Sci & Engn Bengaluru 560056 Karnataka India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Diabetic Retinopathy (DR), one of the main causes of blindness in the eyes, is curable in its early stages. the first indicators are red lesions, which include both micro aneurysms (MAs) and hemorrhages (HEs). Diabetic Retinopathy diagnosis by color fundus images is challenging and time-consuming as it takes trained doctors to recognize the presence and significance of many characteristics, as well as a complex classification method. Even though various machine learning based automatic Diabetic Retinopathy diagnosis approaches exist, they are still unable to offer point-of-care diagnosis, which would allow one to upload an input image, segment the retinal blood vessels, examine each vessel's expansion, and provide accurate result. machine learning and Deep learning techniques have swiftly gained traction to understand medical images with promising results. machine learning can quantify and get more accurate results for the challenges mentioned above, as well as calculate the severity level of the given input eye image as an accuracy rate or predict Diabetic Retinopathy through feature extraction. In this research, a study is conducted to aid in the development of better Diabetic Retinopathy based models, with the aim of enhancing the detection accuracy of Diabetic Retinopathy by evaluating existing machine learning models and mobile applications with multiple datasets.

关键词： Diabetic Retinopathy (DR) machine learning(ML) Deep learning(DL) Image processing Mobile Systems Survey

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