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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1863 条记录，以下是351-360 订阅

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A Comparative Study of Prediction of Pneumonia and COVID-19 Using Deep Neural Networks 3

A Comparative Study of Prediction of Pneumonia and COVID-19 ...

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3rd international conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2023

作者： Haque, Md Sabbirul Taluckder, Md Siam Bin Shawkat, Siam Shahriyar, Md Ahnaf Sayed, Md Abu Modak, Chinmoy Ieee Professional Community Ieee Lamar University Phillip M. Drayer Department of Electrical Engineering United States American International University Computer Science Department Bangladesh College of Technology and Engineering Westcliff University IrvineCA United States New Jersey City University Jersey City Department of Professional Security Studies NJ United States Florida Polytechnic University Department of Computer Science LakelandFL United States

ISBN: (纸本)9798350327762

Pneumonia, a contagious respiratory infection caused by both bacteria and viruses, carries significant global implications. Rapid identification of infected individuals is paramount to effectively curb its transmission. This study explores the application of medical image analysis to address this challenge by proposing machine learning techniques for pneumonia prediction using chest X-ray images. Chest X-ray imaging, known for its accessibility and cost-effectiveness, plays a pivotal role in pneumonia diagnosis. However, the interpretation of X-rays for pneumonia detection can be intricate due to potential overlaps with radiographic features of other respiratory conditions. In this research, various machine learning models, including DenseNet121, Inception Resnet-v2, Inception Resnet-v3, Resnet50, and Xception, are evaluated and compared using chest X-ray images from pneumonia patients. Performance metrics and confusion matrices are employed to assess and differentiate these models. The findings highlight DenseNet121 as the top-performing model, achieving an impressive accuracy rate of 99.58%. This study underscores the significance of machine learning in accurately detecting pneumonia through the analysis of chest X-ray images. It offers valuable insights into the potential of technology to mitigate pneumonia's spread by facilitating precise diagnostic tools. © 2023 IEEE.

关键词： Cost effectiveness

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Diabetes Prediction based on Supervised and Unsupervised learning Techniques - A Review 3

Diabetes Prediction based on Supervised and Unsupervised Lea...

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3rd international conference on Smart Electronics and Communication, ICOSEC 2022

作者： Sivaraman, M. Sumitha, J. Dr. Sns Rajalakshmi College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Diabetes is a disorder that develops in the human body when blood glucose or sugar levels are extremely high. machine learning (ML) is subfield of Artificial Intelligence (AI) that is built on the idea that systems and machines will evaluate and interpret data, learn from it, and make a decision with no human participation. Medical fields that operate with massive amounts of data have recognized by the of machine learning technology. Health care systems can work more effectively and gain a competitive advantage by extracting insights from this data. The general goal of this study is to discover and observe the working and performance of various machine-learning algorithms. This paper work discusses the possibilities of employing machine-learning technology in healthcare. © 2022 IEEE.

关键词： Data mining

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Impact of machine learning in Digital Marketing applications 3

Impact of Machine Learning in Digital Marketing Applications

引用

3rd international Informatics and Software Engineering conference, IISEC 2022

作者： Gursoy, Gunes Varol, Asaf Varol, Serkan Maltepe University College of Engineering and Natural Sciences Dept. of Computer Engineering İstanbul Turkey The University of Chattanooga College of Engineering and Computer Science Dept. of Engineering Management and Technology ChattanoogaTN United States

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

The devastating Covid 19 pandemic has shifted priorities in the business world to accommodate the new normal that the pandemic has caused. Digital marketing has become a necessity for corporations to keep up with the evolving needs of consumers. With the help of the emerging technologies, corporations have begun embracing tailored digital marketing applications that aim to attract and retain more consumers. The techniques or applications used by businesses to improve their marketing performance are consolidated and supported by artificial intelligence applications. In this study, we examine the significance of the marketing field and machine learning, as well as marketing techniques, artificial intelligence, and machine learning algorithms. As a result of the examinations, it has been shown how machine learning techniques and algorithms facilitate marketing processes, increase the profitability of businesses and it is very difficult to realize digital marketing without machine learning algorithms. © 2022 IEEE.

关键词： Marketing

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Predicting Crop on the basis of Climatic Parameters using machine learning Approaches 3

Predicting Crop on the basis of Climatic Parameters using Ma...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Negi, Harendra Singh Dimri, Sushil Chandra Kumar, Bhawnesh Dobhal, Dinesh C. Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9781665462006

India as a country is largely dependent on agriculture. New techniques are being implemented by government and farmers for increasing crop production and crop yield. AI and machine learning are most popular technologies that are being used by government and big private firms for predictions in various sectors like agriculture, healthcare, auto mobile etc. The yield is dependent on factors like weather, fertilizer, pesticide, soil and rain. This article discusses about implementation of machine learning models in agriculture where we are predicting crop that is suitable for the soil at a particular place keeping in mind nutrients and weather of that place. This work will help farmers who are new and will guide them for showing crops by the help of machine learning, which is most advanced technologies to predicates the crop for future forecasting. © 2022 IEEE.

关键词： machine learning

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machine learning based Intelligent Health Monitoring System 3

Machine Learning based Intelligent Health Monitoring System

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Ainapure, Bharati Patrikar, Prajakta Mehta, Aanchal Jain, Aniket Shah, Sanika Vishwakarma University Department of Computer Pune India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

In today's unprecedented time, it is essential to stay one step ahead in the field of healthcare as well as health intelligence by detecting and determining health related problems beforehand and taking swift decisions. However, due to a hectic schedule, it is not always possible for people to go to a medical appointment in person. This may cause therapy to be delayed, and it may eventually become the source of serious health problems. Proposed machine learning based health monitoring system is an android-based smart application that will not only assist users in identifying an illness based on symptoms but will also allow them to communicate with a professional. When a patient enters his or her symptoms, the system uses machine learning and deep learning algorithms to diagnose the illness. The system will offer a list of doctors who specialize in similar conditions based on the disease that has been diagnosed. Users can also arrange an appointment with a doctor at a time and date that is convenient for them. Procuring consultations by utilizing the chatting is also possible. © 2022 IEEE.

关键词： Data mining

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Cardiotocography Fetal Health Data Analysis Using machine learning 3rd

Cardiotocography Fetal Health Data Analysis Using Machine Le...

引用

The 3rd international conference on Frontiers in Computing and Systems (COMSYS 2022)

作者： Singha, Anu Venkateswaran, Vanitha Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi-NCR Campus Uttar Pradesh Ghaziabad India Lead L&T Technology Services Chennai India

ISBN: (纸本)9789819926794

Obstetricians are operating a traditional way to analyze cardiotocography to measure fetus’s physical health stage throughout the prenatal period. During this period, cardiotocography can collect data on the fetal heartbeat and intra-uterine contractions that obstetricians used to detect whether the fetus is normal or pathologic. But this process for traditional obstetricians is taking time-consuming and unreliable. Therefore, this paper proposed a schematic computer-aided system by utilizing the supervised machine learning concept to classify fetal health data. The proposed system implemented a total of eight machine learning models followed by a voting technique to give robustness to predict the accurate class. This study has performed superior with an accuracy of 97%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Supervised learning

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Enhancing Fraud Detection in Transactive Energy Markets: Investigating the Impact of Data Augmentation on the Performance of machine learning and Deep learning Techniques 3

Enhancing Fraud Detection in Transactive Energy Markets: Inv...

引用

3rd international conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2023

作者： Afzoon, Fatemeh Shahinzadeh, Hossein Moazzami, Majid Yaici, Wahiba Karimi, Ali Ebrahimi, Farshad K. N. Toosi University of Technology Department of Electrical Engineering Tehran Iran Amirkabir University of Technology Department of Electrical Engineering Tehran Iran Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran CanmetENERGY Research Centre Natural Resources Canada Ottawa Canada Georgia Institute of Technology Dept. of Electrical and Computer Engineering AtlantaGA United States University of Houston Dept. of Electrical and Computer Engineering HoustonTX77004 United States

ISBN: (纸本)9781509062805

The rise of e-banking has resulted in an increase in fraudulent transactions within Energy Markets, emphasizing the critical need for robust fraud detection systems in financial institutions. A major challenge in fraud detection is the prevalent class imbalance issue found in the datasets used for analysis. Previous research has employed diverse methodologies to develop effective classification models. This study investigates the impact of data augmentation techniques on the performance of both traditional machine learning methods and deep learning methods, aiming to enhance fraud detection capabilities. To accomplish this objective, widely used machine learning techniques, including decision trees, support vector machines, and random forests, are combined with two deep neural network models. Through an extensive series of experiments, the findings demonstrate a significant improvement in the performance of the random forest model when data augmentation is applied. This improvement is measured using the F1-Measure metric and surpasses the performance of the other compared methods. Additionally, the results indicate that, overall, the inclusion of data augmentation enhances model performance in terms of recall but may have an impact on precision. Notably, the performance of deep learning methods is negatively affected by the application of data augmentation. The research contributes to the development of more accurate fraud detection systems in energy markets, benefiting financial institutions and energy market operators in combating fraudulent activities and strengthening transaction security and reliability. © 2023 IEEE.

关键词： Decision trees

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Cyber Threat Detection in Software-Defined Networks: An Empirical Analysis of machine learning Methods 3

Cyber Threat Detection in Software-Defined Networks: An Empi...

引用

3rd international conference on Innovative Mechanisms for Industry applications, ICIMIA 2023

作者： Sharma, Ishu Saini, Jiya Chhabra, Gunjan Kaushik, Keshav Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India University of Petroleum and Energy Studies School of Computer Science Dehradun India

ISBN: (纸本)9798350343632

The field of cyber threat detection in Software-Defined Networks (SDNs) is explored in this research article, which provides a thorough model supported by machine learning techniques. In order to strengthen network security, the model installs AI-Enabled Servers at the application and control levels. These servers are designed to quickly detect and neutralize any threats. These servers are equipped with six different classifiers that enable them to make well-informed decisions based on a range of network metrics: Random Forest, K-Nearest Neighbor, Support Vector machine, Naïve Bayes, Decision Tree, and Logistic Regression. The first line of protection in the model is provided by an initial firewall, which is followed by AI-Enabled Servers for quick threat detection. The research work includes empirical analysis that demonstrates the effectiveness of the model and suggests future directions for practical application, improved threat detection, adaptable security policies, ethical considerations, resource optimization, and industry expert collaboration - all of which will further the search for reliable SDN security solutions. Empirical investigation has confirmed the robustness of the proposed model and shown its effectiveness in detecting and mitigating cyber risks in SDN systems. AI-Enabled Servers are able to make well-informed decisions and adjust to changing network conditions by being trained on a variety of network metrics, such as Port Duration Time, Port Bytes, Port Packets, RX Errors, TX Errors, Port Collisions, Table Active Count, Table Lookup Count, and Table Matched Count. © 2023 IEEE.

关键词： Decision Tree Intrusion Detection K-Nearest Neighbor Naïve Bayes Random Forest Classifier Software Defined Networking

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Research on the Impact of MALL on Oral English Blended learning under the Background of "Internet Plus Education" : - Taking "FiF Oral English Training System" of iFLYTEK as an Example 3

Research on the Impact of MALL on Oral English Blended Learn...

引用

3rd international conference on Education, Knowledge and Information Management, ICEKIM 2022

作者： Du, Xiaoxuan Dalian Neusoft University of Information School of Foreign Languages Dalian China

ISBN: (纸本)9781665416665

Under the background of "Internet Plus Education", the Internet plays a more important role in oral English learning. With the rapid development of technology, the machine-aided learning has developed from computer assisted language learning (CALL) to the stage of mobile assisted language learning (MALL). At the same time, blended learning, which combines online and face-to-face learning, is also widely adopted. This paper makes use of contrast tests to analyze the pretest and posttest result statistics, aiming to research on the impact of MALL on oral English blended learning. © 2022 IEEE.

关键词： computer aided instruction

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Prediction of rainfall-type debris flow in Jiangjiagou based on LSTM-Attention 3

Prediction of rainfall-type debris flow in Jiangjiagou based...

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3rd international conference on computer Vision, Image and Deep learning and international conference on computer Engineering and applications, CVIDL and ICCEA 2022

作者： Gao, Yuan Li, Yingna Kunming University of Science and Technology College of Faculty of Information Engineering and Automation Kunming China Key Laboratory of Computer Technology Application of Yunnan Province Kunming China

ISBN: (纸本)9781665459112

Through the neural network model, the rainfall-type debris flow in Jiangjiagou is predicted. Based on seven meteorological factors with time series characteristics, such as daily rainfall, previous effective rainfall, ground temperature, and humidity, which have been screened by features, the Jiangjiagou rainfall debris flow prediction and early warning model based on LSTM-Attention is constructed. The results show that: (1) LSTM-Attention model is 0.02 higher than that of the Attention-LSTM model, and the prediction effect is better. (2) The prediction accuracy of LSTM-Attention model for Jiangjiagou rainfall-type debris flow can reach 0.88, which is 0.05, 0.07, 0.04, 0.25, 0.13 higher than that of LSTM model, RNN model, RNN-Attention model, SVM model, and BP model, respectively. Research results: It provides a new idea and method for the prediction and research of rainfall-type debris flow, which can be referenced by scholars. © 2022 IEEE.

关键词： Long short-term memory

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