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

共 1897 条记录，以下是421-430 订阅

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Comparative Analysis of machine learning Algorithms for Securing IoT Enabled Environment 3

Comparative Analysis of Machine Learning Algorithms for Secu...

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

作者： Sagu, Amit Gill, Nasib Singh Gulia, Preeti Rani, Deepti Chahal, Ayushi Maharshi Dayanand University Department of Computer Science and Applications Rohtak India

ISBN: (纸本)9781665462006

The IoT (Internet of Things) link system, various applications, data storage like cloud, and another service area that possibly will be a fresh entry for attackers as they uninterruptedly offer services in the organization. At this time, threats to users' privacy and malware pose significant challenges to the integrity of the Internet of Things. These extortions might lead to the loss of important information, which in turn could cause a company's finances and reputation to suffer. In this paper, we have identified anomalous activity throughout the IoT ecosystem by utilizing a variety of machine learning approaches. The results from the experiment indicate that the categorization capabilities of machine learning techniques may serve as an alternative strategy for ensuring the safety of communication inside the IoT. © 2022 IEEE.

关键词： Internet of things

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Hybrid machine learning based Intrusion Detection System for IoT 3

Hybrid Machine Learning based Intrusion Detection System for...

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

作者： Mittal, Kajol Batra, Payal Khurana Jaypee Institute of Information Technology Department of Computer Science and Engineering Noida India

ISBN: (纸本)9781665462006

The Internet of Things (IoT) is susceptible to several attacks by insiders and outsiders. IoT requires an effective Intrusion Detection System (IDS). The development of IDSs that can quickly and automatically identify and classify cyberattacks at the network and host level commonly uses machine learning techniques. This research proposes a hybrid machine learning model that combines Naive Bayes and K-NN. The UNSW-NB15 dataset, open to the public, is used to test the suggested model. Accuracy, precision, recall, and f1-Score are four well-known performance evaluation metrics used to analyze the model's performance. The experimental results show that the hybrid approach performs better than the individual ML model, with the accuracy of 91.8% in the case of multiclass classification and 96.2% in the case of binary classification. © 2022 IEEE.

关键词： Internet of things

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Development of machine learning Model for Detection and Diagnosis of Alzheimer's disease. A Comprehensive Review 3

Development of Machine Learning Model for Detection and Diag...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Khan, Safdar Sardar Patil, Sunil Rkdf University Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350343274

New techniques of detection and diagnosis are needed for Alzheimer's disease (AD) is a major concern for society's health. machine learning (ML) has been identified as a viable approach in this context due to its capacity to allow the development of predictive models that can help in the identification of AD across its various phases. This paper provides an overview of current developments in developing ML models for application in Alzheimer's disease screening and detection. The paper opens with a discussion of the importance of early identification and recognition of Alzheimer's disease by detailing the disease's pathophysiology and describing its clinical signs. Data from neuroimaging (e.g., MRI, PET scans), clinical data (e.g., cognitive evaluations, genetic information), and new biomarkers (e.g., markers in cerebrospinal fluid) are all discussed next. The report then delves into the development of ML methods in AD studies, showing the shift from classical statistical approaches to cutting-edge algorithms like deep learning and ensemble approaches. This article delves into the significance of feature selection and extraction in improving model performance and readability. Moreover, the paper discusses difficulties connected to the accessibility of data, the presence of bias, and the requirement of standardized datasets and evaluation criteria. The review's meat and potatoes are an in-depth look at the latest research and top-tier ML models for AD diagnosis and detection. There is a large variety of techniques that may be used to these models, including as Transfer learning from different domains, semi-supervised learning, supervised learning, and unsupervised learning. The article also discusses how multimodal data fusion techniques could help improve diagnostic precision. The evaluation places equal emphasis on the interpretability and therapeutic usefulness of models as they do on their development. The article talks about how to incorporate model predictions

关键词： Genes

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An ensemble learning-based model for effective chronic kidney disease prediction 3

An ensemble learning-based model for effective chronic kidne...

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

作者： Kumari, Sweety Singh, Sunil Kumar Mahatma Gandhi Central University Dept. of Computer Science and Information Technology Bihar Motihari India

ISBN: (纸本)9781665462006

Due to rising chronic kidney disease (CKD) cases across the globe, it is required to be detected and diagnosed effectively. machine learning-based models can be an effective tool for early and effective predictions. In this work, we apply machine, deep, and ensemble learning-based models to predict the cases of CKD and non-CKD. We have also applied the stacking and voting ensemble of SVM, RF, Adaboost, LDA, and MLP models to effectively and quickly predict the CKD and non-CKD cases. Accuracy, Precision, Recall, F1-Score, and ROC are used to evaluate the models' performance. Further, the Freidman ranking is also calculated to measure the effectiveness of the models. A comparative study concludes that ensemble learning-based models are the best-performing models. © 2022 IEEE.

关键词： learning systems

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Research on Pattern Recognition and Retrieval of "Reliance" Based on Deep learning 3

Research on Pattern Recognition and Retrieval of "Reliance" ...

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3rd international conference on Artificial Intelligence and computer Engineering, ICAICE 2022

作者： Zhang, Chunxia Zhang, Guoyun Krirk University Bangkok10220 Thailand

ISBN: (纸本)9781510663473

With the rapid development of computer vision technology in the field of visual fashion, more and more people pay attention to the research on the "reliance" pattern of drama clothing. At present, in the field of clothing image display, research mainly focuses on clothing image recognition, key point detection, clothing recommendation, retrieval and matching. These studies can provide decision support for the design, production, display, sales and other links of drama costumes and bring a new display experience. However, in the realistic application scenarios of clothing "reliance" images, we still face challenges brought by changes in clothing style, materials, cutting, pattern composition and combination methods, which make the effects in recognition, positioning, recommendation and other applications continuously improved through experiments. The method based on depth learning in this paper focuses on clothing "reliance" pattern recognition, key point detection, clothing retrieval and other tasks. © 2023 SPIE.

关键词： Image recognition

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machine learning-Powered Real-Time Motion Detection System: A Review 3

Machine Learning-Powered Real-Time Motion Detection System: ...

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

作者： Khurana, Naman Bansal, Madhavi Thakur, Ananya Varshitha, Velma Sai Tannu Kaur, Er. Kirat Chandigarh University Computer Science & Engineering Punjab India

ISBN: (纸本)9798350343632

Motion detection, a fundamental technology in computer vision, is crucial for a wide array of applications, including security,surveillance, gaming and robotics. OpenCV, the open-source computer Vision Library, has emerged as a versatile tool for developing robust and efficient motion detection systems. The article offers insights into the intricacies of motion detection using OpenCV, combining existing research findings with a unique perspective on this critical subject. It explores traditional and advanced techniques, highlights real-world applications and addresses persistent challenges in the field such as handling background clutter, adapting to changing lighting conditions, and achieving real-time processing. It presents a fundamental approach to motion detection, encompassing frame acquisition, frame differencing, thresholding, noise reduction, object tracking, and more, with a focus on the tools and technologies that can enhance these processes. The primary problem addressed in the article is the development of accurate, reliable, and real-time motion detection systems considering various challenges and objectives across different application domains. The provided references encompass a wide range of research in the field, contributing to a comprehensive understanding of motion detection. The article can serve as a valuable resource for individuals interested in motion detection and its integration with OpenCV, offering a foundation for readers to embark on their motion detection projects and foster innovation in this dynamic field. © 2023 IEEE.

关键词： computer Vision Frame Differencing Motion Detection Object Tracking Thresholding

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machine and Deep learning Techniques to Classify and Predict Thyroid Diseases 3

Machine and Deep Learning Techniques to Classify and Predict...

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

作者： Gosain, Monika Gupta, Shikha Kaur, Sukhpreet Chandigarh University Department of Computer Science and Engineering Gharuan India Department of Computer Science and Engineering College Landran India Chandigarh Engineering College Department of Computer Science and Engineering Landran India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Thyroid disease is a disorder that affects people globally, with women suffering from it at a higher rate than males. Despite having various technologies, it is still difficult to recognize or diagnose such disease early due to the enormous number of cases that emerge each year. As far as artificial intelligence is concerned, it has greatly contributed to the health business by assessing a wide range of illnesses and offering the greatest possible help to patients, doctors, lab workers, and others. Hence, in this review study, we have investigated numerous researchers' approaches for recognizing various thyroid disorders utilizing machine and deep learning models. Moreover, the report had also mentioned their contribution and the challenges that still persist in this field. Our analysis of the different methods proposed has been also provided to draw some conclusions. © 2022 IEEE.

关键词： Deep learning

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Integration Of Computational With Physical System Through machine learning 3

Integration Of Computational With Physical System Through Ma...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Ibayevich, Mahsimov Qosimkhon Rahimjonovna, Khakimova Kamolatkhon Abduraufovich, Kayumov Odiljon Khudoynazarovich, Turdikulov Khusanboy Department of Geodesy Cartography and Cadastre Fergana Polytechnic Institute Fergana Uzbekistan

ISBN: (纸本)9798350399264

The transition of a rice machine mixture from a stand - alone application to a fully integrated manufacturing system is described in this study. In this procedure, load cells that had already been installed situ were switched out, and extra sensitive and electromagnetic translation gauges were used. The Smart Joining Lab at the Division of Metallic Making completely incorporated that one after calibrating into a six-layer democratization framework. Two front-end mortal interactions were created inside this architecture, with the first one acting as a situation tracking system during the turning operation. A robust machine-learning algorithm that was created using Python and is able to not only forecast and adjust the final rolling program of a specified metal sheet but also learn from further steel production timetables executed is visualized in the main graphical interface. Utilizing data from some of the more than 1900 milled steps with various roll spacing height, sheeting size, and roughness parameters, this algorithm was developed using a black box methodology. As a consequence, the created software may extrapolate and parallelize between any of these characteristics as well as various beginning panel depths, acting as a digital system for suggestions based on data on scheduling adjustments for various trying to roll production steps. Additionally, it is conceivable to see how the settings affect the outcome of the milling operation using the third gui. Schoolchildren and other stakeholders can open the viewing because the entire layer system is running on a community college existing system. They could use surroundings to further their understanding of the features and influences of the sheet metal forming as well as computer science and particularly the fundamentals of computer science. This methodology also forms the foundation for the future integrating of data from the field of nanomaterials for the forecasting of the impact of various materials on the rol

关键词： MATLAB

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A Fusion Neural Network Incorporating Attention for Sensor-Based Human Activity Recognition 3

A Fusion Neural Network Incorporating Attention for Sensor-B...

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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： Lu, Limeng Zhang, Chuanlin Cao, Kai Deng, Dao Institute of Chinese Ethnic Information Technology Northwest Minzu University Lanzhou China Key Laboratory of China's Ethnic Languages and Information Technology Ministry of Education Lanzhou China Northwest Minzu University School of Mathematics and Computer Science Lanzhou China Northwest Minzu University Key Laboratory of Streaming Data Computing Technologies and Application Lanzhou China

ISBN: (纸本)9781665459112

Even though the RNN, LSTM, and other networks are used to extract dependencies in time series, sensor-based human behavior recognition (HAR) still faces some difficulties, and the ability of deep learning (DL) networks to extract features still needs to be improved. We propose a fusion neural network in which an optimized small optimized Convolutional Block Attention Module (MP-CBAM) is suitable for HAR tasks to extract samples. The MP-CBAM is added to two branches Convolutional Neural Network (CNN) with different convolution kernel sizes, and the fused features are labeled with GRU for temporal dependence. Then the softmax function is used for classification. We validate on benchmark datasets UCI-HAR and WISDM and accuracies of 97.15% and 9S.9S% are obtained, respectively, demonstrating the adaptation of our framework to both single-sensor and multi-sensor devices. © 2022 IEEE.

关键词： Convolution

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Facial Emotion Recognition and Detection Using Convolutional Neural Networks 3

Facial Emotion Recognition and Detection Using Convolutional...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Dalal, Renu Khari, Manju Pandey, Priyank Jatana, Samanvay Joshi, Vijay Usar Guru Gobind Singh Indraprastha University East Delhi Campus Surajmal Vihar New Delhi India Jawaharlal Nehru University Department of Computer and Systems Science New Delhi India Graphic Era Deemed to Be University Department of Computer & Engineering Dehradun India Maharaja Surajmal Institute of Technology Ggsipu Department of Information Technology Janakpuri Delhi India

ISBN: (纸本)9798350319125

Facial emotion recognition is now becoming a hot topic in previous years as it helps in human computer interaction. As human emotions can be detected, there are various studies on it in the computer vision field. Humans can easily recognize the type of emotion from someone's facial expressions but it's tough for a computer machine. FER can be used in vast areas for applications such as education, social media, healthcare, Al based application, etc. In this research paper, the Convolutional Neural Network (CNN) technique has been built. The methodology is mainly divided into two steps: Haar cascade features from the face are extracted by using Viola Jones algorithm leading to facial identification and then facial emotion is recognised by using the help of neural network. This technique is built on conventional neural networks (CNN) and it has used FER2013 and CK+ dataset for detection of human emotions. This model consists of 3 completed connected layers and six convolutional layers. On the basis of our experimental results, this model has a validation accuracy of 68.26% with FER 2013 dataset and 91.58% with CK+ dataset. © 2023 IEEE.

关键词： CNN Deep learning Facial Emotion Recognition Human computer Interaction

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