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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 769 条记录，以下是181-190 订阅

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Ensemble of CNN and DenseNet121 Models for Pneumonia Prediction Using X-Ray Images 6

Ensemble of CNN and DenseNet121 Models for Pneumonia Predict...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Saxena, Suryansh Maji, Srabanti Agarwal, Aashray Goel, Anshul School of Computing DIT University Uttarakhand Dehradun India

ISBN: (纸本)9798350304480

When left untreated, pneumonia, a respiratory infection, can result in serious sickness or even death. In recent years, promising findings have been obtained in the accurate identification of pneumonia utilising X-ray image processing methods based on machine learning. In this article, we provide a deep learning-based approach for predicting pneumonia from X-ray images. We applied the averaging technique to combine CNN and DenseNet121, two well-known deep learning models, using a dataset from Kaggle. According to our tests, the CNN model had a test accuracy of 91.2% whereas DenseNet121 had an accuracy of 89.1%. the ensembled model's accuracy of 90.2 % shows how assembling can improve the accuracy of individual models. Our findings imply that deep learning models may prove to be a valuable tool for the precise and early detection of pneumonia, which may lead to better patient outcomes. © 2023 IEEE.

关键词： Artificial intelligence CNN formatting insert Lung disease detection machine learning neural networks Pneumonia detection using machine learning style styling

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Prediction of Dermatological Disease with machine learning 6

Prediction of Dermatological Disease with Machine Learning

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Rajput, Pranchal Pramanik, Atreyi Chanti, Yerrolla School of Applied and Life Sciences Division of Research and Innovation Uttaranchal University Uttarakhand Dehradun India School of Computer Science & Artificial Intelligence SR University Andhra Pradesh Warangal India

ISBN: (纸本)9798350304480

Dermatological disorders (DD) are widespread health issues in nations worldwide. the dangers of infections are hidden, and may lead to both mental and physical health problems. In severe circumstances, it might even result in skin cancer. therefore, one of the most difficult jobs in medical image processing is to diagnose DD from clinical photographs. Additionally, diagnosing DD manually by medical professionals is time-consuming and arbitrary. therefore, automatic DD prediction is necessary for both patients and dermatologists, which speeds up the planning of therapies. In this piece of writing, we reviewed the different types of DD, procedure uses for the identification and categorization of DD and their early identification by the help of various machine learning (ML) algorithms which gives their specific accuracy rate in which Convolutional Neural Network (CNN) shows the highest accuracy with the accuracy rate 99.05% respectively. © 2023 IEEE.

关键词： Algorithms Dermatological disorder machine learning Prediction skin cancers

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Analysis of Cardiovascular Arrhythmia Prediction Using Different machine learning Models 6

Analysis of Cardiovascular Arrhythmia Prediction Using Diffe...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Keerthana, N. Ashwin Kumar, V. Veena, S. PES University Department of Electronics and Communication Karnataka Bengaluru India Covalensedigital Karnataka Bengaluru India

ISBN: (纸本)9798350304480

Cardiac arrhythmia are aggravations in the pattern of heart, brought about by anomaly or strange quick or slow pulses. the scope of cardiovascular arrhythmias incorporates conditions that are promptly hazardous and guiltless extrasystoles. the exhaustive assessment of an ECG of the greatest conceivable quality gathered during the arrhythmia is fundamental for the precise recognizable proof of heart arrhythmias. In past few years the exploration has been seen from feature grouping approach to deal with classifier approaches. the conduct of traditional classifiers such LR (Logistic Regression), DT (Decision Tree), SVC (Support Vector Classifier) etc. are more depended on pre-filtering and attribute extraction modules, while the performance of latest classifiers like DNN (Deep Neural Networks), CNN (Conventional Neural Networks) can create attributes that includes consequently from ECG signal. this paper concentrates on evaluating classification accuracy analysis of traditional and latest approaches using the PTB Diagnostic ECG dataset and the MIT-BIH Arrhythmia dataset. © 2023 IEEE.

关键词： Accuracy Cardiac Arrhythmia Classification CNN DNN ECG ML

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Detecting the Stage of Breast Cancer Using machine learning Algorithms 6th

Detecting the Stage of Breast Cancer Using Machine Learning ...

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6th international conference on Soft Computing and signal processing, ICSCSP 2023

作者： Vennela, Boya Anuhya, Pittala Ramaswamy, T. Subba Rao, S.P.V. Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad India

ISBN: (纸本)9789819986279

the cancer type that affects women the most frequently diagnosed today is breast cancer. It manifests in the breast tissue and is one of the top reasons of death for women. this malignancy is curable if it is discovered when it is still treatable. Regarding the instance of breast cancer, there are two different types of tumors: benign and malignant. Cancerous tumors are fatal because they develop significantly more quickly than benign tumors do. therefore, early tumor type identification is crucial to ensuring that a patient with breast cancer receives the best possible care. the Wisconsin Bosom Malignant Growth Dataset, which was used in this work, came from the UCI repository. the dataset will be examined to see which artificial intelligence algorithm is better at foretelling breast cancer. In this case, classifiers including backing vector machine, strategic relapse, k-nearest neighbors, choice tree, credulous Bayes, and irregular backwoods have been used to distinguish between benign and dangerous growths. To choose the optimal algorithm, each accuracy is calculated and contrasted. the investigation demonstrates that with an accuracy of 96.5%, random forest and support vector machine perform better than other classifiers. Using these classifiers, one may create a pre-programmed indicative framework for diagnosing breast cancer. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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A machine learning based Facial Expression and Emotion Recognition for Human Computer Interaction through Fuzzy Logic System 6

A Machine Learning based Facial Expression and Emotion Recog...

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6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Vinutha, K. Niranjan, Manoj Kumar Makhijani, Jagdish Natarajan, B. Nirmala, V. Vijaya Lakshmi, T.R. Department of Information Science and Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Department of Computer Applications Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Rustamji Institute of Technology Madhya Pradesh Gwalior India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India Department of Science and Humanities Faculty of Engineering Karpagam Academy of Higher Education Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350398496

Facial recognition is in use for the past decade there are many applications that needs facial expression to learn the human behaviour and emotions for certain activities. Facial recognition is in a development phase where many service providers use this feature to find the expression of the people on using their BlogSpot or website or reading any news article. this recognition of facial expression is highly possible with the help of machine learning technology. this research study has developed a facial expression recognizing algorithm using Python programming language with the help of Keras software package. this algorithm is purely based on machine learning approach that enables the programmer to process the facial image and convert it into data that is helpful in prediction of facial expression using the fuzzy logic technique. the fuzzy logic technique is a prediction method that helps programmer to predict the intermediate data by providing the initial and ending conditions. For enabling the facial recognition to process any system or a mobile device the algorithm needs permission to access the camera, once the onto the access is permitted the algorithm retrieves the image from the Vision sensor and with the help of image processing technology of the machine learning algorithm the program the program converts the data from the vision sensor into required facial expression and emotional content. © 2023 IEEE.

关键词： Emotion Recognition

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Planet Optimization with machine learning Enabled Power Usage Forecasting Modeling in Smart Grid Environment 6

Planet Optimization with Machine Learning Enabled Power Usag...

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6th Iraqi international conference on Engineering Technology and its Applications, IICETA 2023

作者： Almoussawi, Zainab Abed Kurdi, Waleed Hadi Madhloom Khaleel, Basma Mohammed Al-Attabi, Kassem Sabah, Hawraa Ali Alazzai, Waleed Khalid College of Islamic Science Ahl Al Bayt University karbala Iraq Altoosi University College Najaf Iraq Al Rafidain University College Computer Technology Engineering Department Bagdad Iraq The Islamic University Najaf Iraq National University of Science and Technology Dhi Qar Iraq Medical Technical College Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350303339

the smart grid (SG) significantly enhances the conventional power grids with information and communication technologies, control decision-making systems, simulation analysis, advanced measurement and sensing. In contrast to conventional power grids, the SG has further benefits in stability, situational awareness, self-healing, information interaction, and renewable energy consumption. But the precise prediction of long-term electric energy consumption remains a challenge. Recently, machine learning (ML), particularly deep learning, has rapidly advanced and has demonstrated outstanding performance in various tasks of the SG fields. the representation capability of ML approaches is considerably enhanced. the SG is a curial framework area, hence ML algorithms including it should be interpretable for improving reliability of the system and increasing user trust. this study develops a new Planet Optimization with machine learning Enabled Power Usage Forecasting Modeling (POML-PUFM) in SG environment. the presented POML-PUFM technique forecast the utilization of power in the SGs for smart city applications. In the presented POML-PUFM technique, data pre-processing takes place to transform them into compatible format. For forecasting purposes, the POML-PUFM technique employs twin-support vector machine (TWSVM) model. In addition, the study used PO model to adjust the parameter related to the TWSVM method. the prediction performance of the presented POML-PUFM algorithm is examined and the outcomes are examined under several measures. the study verified the improvements of the POML-PUFM technique over other ML models. © 2023 IEEE.

关键词： Forecasting

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Prediction of Internal Moisture in Post-Harvest Cocoa (theobroma Cacao) Using a machine learning System, Applicable to Farms that Use Solar Drying 6

Prediction of Internal Moisture in Post-Harvest Cocoa (Theob...

引用

6th IEEE international conference on BioInspired processing, BIP 2024

作者： Zarate-Montero, German Aguirre, Carlos Hernandez Guevara, Alejandra Mencia Espinoza, Gerald Torrentes Montero, Luis Zarate Escuela de Ciencias Agrarias UNA Laboratorio de Calidad e innovación agroalimentaria Heredia Costa Rica Escuela de Química Escuela de Química UNA Heredia Costa Rica Solvosoft Heredia Costa Rica

ISBN: (纸本)9798350353709

this article explores the potential for predicting moisture content in cocoa post-harvest using a machine learning system that incorporates experimental data and accounts for varying temperature conditions. Key postharvest processes, such as fermentation and drying, significantly influence the quality of the final product, but humid and rainy climates in cocoa-growing regions complicate these processes, necessitating improved technologies. the methodology involves developing a prediction engine utilizing a Multilayer Perceptron (MLP) with MLPClassifier and MLPRegressor classes, employing experimental field data and two data-generating models: an automatic moving boundary model for isothermal food dehydration and the Brusselator model for autocatalytic reactions. A total of approximately 2,500 experimental data points were collected from six tests conducted between June and December 2023. the neural network was designed to accept a convergence interval of experimental results for 70 % of the training data, with the remaining 30% used for verification. the prediction engine achieved an accuracy range of 78 % to 82 %. the implementation of Adrover's isothermal dehydration model proved beneficial, preventing non-convergence and showcasing effective convergence-accelerating algorithms. the system can make predictions for up to 420 hours based on experimental results. Ultimately, the study presents a predictor for postharvest cocoa conditions using an artificial neural network that effectively accounts for temperature variability, making it suitable for artisanal cocoa processing. However, it emphasizes the need for additional data to improve mean squared error (MSE) and R2 values, and suggests future research should include genetic diversity of cocoa lots to enhance prediction accuracy. © 2024 IEEE.

关键词： Mean square error

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Fiber-Optic signal Based Deep learning for Sleep Medicine Research 6

Fiber-Optic Signal Based Deep Learning for Sleep Medicine Re...

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6th IEEE international conference on Pattern Recognition and Artificial Intelligence, PRAI 2023

作者： Wang, Hua Li, Chen-Jie Cui, Xiu-Tao Li, Long Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China Shanghai Key Laboratory of Power Station Automation Technology Shanghai200444 China DewertOKIN Technology Group Co. Ltd JiaXing314000 China Bewatec Medical Devices Co. Ltd JiaXing314000 China

ISBN: (纸本)9798350325485

As living standards continue to improve, people are becoming increasingly concerned about their own health and the quality of their sleep. However, currently available sleep monitoring devices often have issues with uncomfortable wear and low data accuracy, which limit users' experience and the effectiveness of data analysis. At the same time, there is increasing attention to real-Time monitoring technology based on human physiological indicators in response to the demand for personalized healthcare management. In this context, fiber optic signal technology has emerged. By utilizing fiber optic sensing technology, it is possible to non-invasively acquire subtle human signals, such as respiratory rate and heart rate. this paper proposes an algorithm combining microprocessor and machine learning technology, which can effectively identify the user's sleep state and Physical change, and improve the accuracy of prediction. © 2023 IEEE.

关键词： Fiber optics

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Advances Toward Word-Sense Disambiguation 6th

Advances Toward Word-Sense Disambiguation

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6th international conference on Innovative Computing and Communication, ICICC 2023

作者： Mir, Tawseef Ahmad Lawaye, Aadil Ahmad Ahmed, Ghayas Rana, Parveen Baba Ghulam Shah Badshah University Jammu and Kashmir Rajouri185234 India

ISBN: (纸本)9789819940707

the process of assigning the correct meaning to a word with respect to its context is known as Word-Sense Disambiguation (WSD). It is a hard problem in natural language processing. In this paper, we discuss various approaches used to tackle this problem. Research works conduced in recent years to solve this problem are also discussed with special focus on resource poor languages. Technological trends followed in the recent years are analyzed that may help in the identification of future path to search better WSD solutions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： machine learning

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Employing deep learning framework for improving solar panel defects using drone imagery 24

Employing deep learning framework for improving solar panel ...

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24th international conference on Digital signal processing, DSP 2023

作者： Terzoglou, G. Loufakis, M. Symeonidis, P. Ioannidis, D. Tzovaras, D. 6th km Harilaou - Thermi Thessaloniki57001 Greece

ISBN: (纸本)9798350339598

this research describes a unique method for identifying and categorizing solar panel problems using RGB and thermal pictures captured by drones. the first step of the suggested technique is to identify solar panels in the photographs by using a CNN based on YOLOv5 architecture model that was trained and tested on an annotated dataset of solar panels. A number of computer vision techniques were used to separate the panels from their backgrounds in order to get around the accuracy issues with the detector. the panels were then classified as normal or anomalous using a state-of-the-art EfficientNet classifier, which was trained on a synthetic dataset. the anomalies were then divided into four categories: cell, multi-cell, diode, and multi-diode. the results obtained from this research demonstrate the viability and potential of employing drones to identify and categorize solar panel problems and emphasize the significance of creating precise models to enhance solar park management. © 2023 IEEE.

关键词： Drones

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