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共 1791 条记录，以下是191-200 订阅

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Diagnosis and Identification of Citrus Canker Growth Rate Using machine learning 4

Diagnosis and Identification of Citrus Canker Growth Rate Us...

引用

4th IEEE international conference on pattern recognition and machine learning, PRML 2023

作者： Zainab, Naureen Afzal, Hammad Arif, Fahim Ghafoor, Abdul Iltaf, Naima Zakria, Muhammad Hyder, Muhammad Javed Ahsan, Rafia National University of Sciences and Technology Department of Computer Software Engineering Islamabad44000 Pakistan Park Road Islamabad44000 Pakistan Department of Mechanical Engineering Islamabad44000 Pakistan

ISBN: (纸本)9798350324303

Citrus canker is a bacterial disease caused by Xanthomonas citri pv citri. Mainly the research in the field of Citrus canker has been done on fruits;early detection of disease on leaves can help in adopting preventive measures at early stage. the present study focuses on the detection of Citrus canker in leaves and classification of six different growth stages/levels of disease: water soaking, yellow initiation, chlorosis, and blister formation, canker infection (50%), canker infection (100%). the primary dataset was developed by taking images of citrus leaves infected by X. citri for the specified time. the proposed strategy is based on the detection of the lesion spot on citrus leaves and the classification of citrus disease stages/level was done to evaluate the growth rate of citrus canker through six distinct growth levels and evaluate affected area of leaf with each passing day. the entire processing includes Pre-processing, Segmentation, Features extraction and perform Classification using four different classifiers which include SVM, KNN, Naive Bayes, and Neural Network. the efficiency of different classifiers was 96.77% by SVM, followed by KNN 90%, NB 84% and NN 82% respectively. the lowest execution time was achieved by NB 0.011s, followed by KNN 0.015s, SVM 0.021s, and N.N. 0.216s. © 2023 IEEE.

关键词： Image classification

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Human Activity Classification Based on data Analysis and Feature Extraction 23

Human Activity Classification Based on Data Analysis and Fea...

引用

4th international conference on Computing, Networks and Internet of things, CNIOT 2023

作者： Liang, Qiao Hu, Cheng Huang, Haiyan Guangdong University of Foreign Studies Guangzhou China Guangzhou City University of Technology Guangzhou China

ISBN: (纸本)9798400700705

Human behavior recognition is one of the most important research directions in the field of computer vision, and it plays an important role in the fields of rehabilitative medicine, auxiliary security, and scene entertainment. To address the shortcomings of traditional HAR recognition methods with tedious feature extraction and severe overfitting, we propose a human behavior recognition model based on XGBoost and feature simplification methods with a limited data set. the model uses the XGBoost algorithm to classify the collected sensor data to recognize human behaviors. In addition, to improve the efficiency and accuracy of the model, we also propose a feature simplification method to reduce the computational complexity and the risk of model overfitting by reducing the number of features. Experimental results show that the model has high accuracy and computational efficiency and can be applied to different human behavior recognition scenarios. CCS Concepts: Computing methodologiesg 1/4machine learningg 1/4machine learning approaches © 2023 ACM.

关键词： Feature extraction

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Deep learning-based vehicle tracking and traffic event detection 4

Deep learning-based vehicle tracking and traffic event detec...

引用

4th international conference on Computer Vision, Image and Deep learning, CVIDL 2023

作者： Sun, Guohao Yan, Kehuan Fan, Chengbin School of Mathematics Tianjin University Tianjing China School of Computer and Data Science Fuzhou University Fujian China School of Electrical Engineering and Automation Xiamen University Tan Kah Kee College Fujian China

ISBN: (纸本)9798350326444

Time-series data mining plays an important role in big data decision making because it can reveal the development pattern of things. Similar concatenation of temporal data is a fundamental prerequisite for data twinning., whose core objective is to find all similar temporal data pairs according to a given similarity metric. Dynamic temporal regularization (DTW) has been widely used in many fields., such as target detection., trend prediction and fault identification., as the best data alignment method on temporal data. We use deep learning to extract vehicle trajectories and perform behavioral pattern learning., and finally use an improved DTW algorithm to pre-process the trajectory data and solve the distance function to achieve matching between the trajectories of the event sequence to be measured and the typical trajectory data patterns. By comparing the indicators with the unimproved DTW algorithm., the research results show that this traffic condition recognition method is stable and reliable., and can maintain high matching accuracy with significantly reduced computation., high success rate and good real-time performance. © 2023 IEEE.

关键词： data mining

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Design and Evaluation of a Transfer learning Model for Ailment recognition in Plant Images 4

Design and Evaluation of a Transfer Learning Model for Ailme...

引用

4th IEEE international conference on data Engineering and Communication Systems, ICDECS 2024

作者： Raja Subramanian, R. Srija, Marni Reddy, Gujula Rohini Krishna, Mangu Sai Sree Prasanna, Bathula Lakshmi Kalasalingam Academy of Research and Education Department of Computer Science and Engineering India

ISBN: (纸本)9798350393354

the agriculture sector is still working to mitigate the detrimental effects of plant diseases on crop production and food security. Accurate and prompt disease diagnosis is essential to preventing extensive crop damage. Deep learning, a subfield of machine learning, has generated a great deal of interest and has astounding potential for automating the identification of plant diseases from images. this article presents a comprehensive overview of the advancements, strategies, and potential applications of CNN for the diagnosis of plant diseases.. the paper covers important facets of the deep learning-based methodology, such as dataset curation and collection, picture preprocessing methods, architectural decisions, and training methods. the effectiveness of CNN models for detecting plant diseases is discussed in this article, with a special emphasis on convolutional neural networks (CNNs) as playing a key role. © 2024 IEEE.

关键词： Convolutional neural networks

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Chinese herbal medicine classification prediction using random forests under the constraint of infrared spectral data 4

Chinese herbal medicine classification prediction using rand...

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4th international conference on Advanced Optics and Photonics Research in Engineering, AOPR 2024

作者： Zhong, Mantian Luo, Zhimin Jiangmen Polytechnic Guangdong Jiangmen529090 China

ISBN: (纸本)9781510682610

the same herbs from various origins were scanned with mid-infrared under standard lighting conditions, and the spectral readings at various wavelengths were gathered, sorted, examined, and downscaled. the downscaled data were then divided into a training set and a test set to form modeling data, which were used to determine known herb origins and predict unknown herb origins using a random forest model in Python and contrasted with a linear support vect. the paper notes that, when comparing the two models, random forest and support vector machine, for herbs of known origin, the random forest model performs better in terms of dimensionality reduction, correct recognition rate in training sets, and correct recognition rate in test sets, by 28.57%, 1%, and 2.9% respectively. the findings demonstrate that the random forest model is deserving of being of significant value in the era of big data to assist clinicians in precisely identifying herbal medicine categories for treatment. It is effective in boosting classification accuracy and lowering computation and time consumption. © 2024 SPIE.

关键词： Random forests data modeling Medicine Education and training Infrared radiation Absorbance machine learning Support vector machines Mid infrared thermal modeling

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Real-Time Human Pose Estimation Using Media-Pipe an Artificial Intelligence Applications in Health and Fitness 4

Real-Time Human Pose Estimation Using Media-Pipe an Artifici...

引用

4th international conference on Artificial Intelligence and Signal Processing

作者： Totlani, Ketan Dhavala, Shiva S. Vijayarao, S. Sandeep Kumar Challagundla, Yagnesh Roy, B. Zhuo, Eugenia R. Jaipur Natl Univ Sch Comp & Syst Sci Jaipur Rajasthan India Rashtreeya Vidyalaya Coll Engn Sch Informat Sci & Engn Bengaluru India RMK Engn Coll Dept Comp Sci & Engn Kavaraipettai Tamil Nadu India VIT AP Univ Sch Elect Engn Amaravati Andhra Pradesh India Univ Santo Tomas Manila Coll Informat & Comp Sci Manila Philippines

ISBN: (纸本)9798350350661;9798350350654

Significant progress has been achieved in the fields of Artificial Intelligence (AI), machine learning (ML), and Deep learning (DL) in recent years. Nevertheless, a significant gap continues to exist in the effective surveillance of human actions, activities, and the recognition of human movements or poses. machines and computers are fundamentally incapable of comprehending human activities without user intervention. Consequently, we now depend on technology to record and archive human actions through live broadcast data, videos, or images. the field of pose estimation, which is a critical area of research with applications in fields such as Augmented Reality, Animation, Gaming, Healthcare, and Sports, is expanding due to the rapid advancement of computer vision technology. this project implements innovative methods to train models and determine poses, utilizing random inputs from internet images, videos, and live streaming. In particular, models that have been trained with artificial intelligence have been created for daily physical activities that necessitate intricate pose estimation. the initial step in the project is to utilize MediaPipe to estimate the poses of the human body. Subsequently, the angles between the body joints are calculated to create the desired models. Additionally, a web-based user interface (WEB-UI) was developed to enable access from any location. the methodology involves the extraction of data from critical body locations, the framing of models, and the utilization of the MediaPipe model due to its superior performance and features in comparison to other models. the objective of this method is to develop a web application for end-users, train AI models for daily physical activities, and establish an efficient system for single human pose estimation. this approach provides a cost-effective alternative to physical trainers and enables the monitoring of health and sports activities to be enhanced.

关键词： Human Pose Estimation Artificial Intelligence machine learning Computer Vision Deep learning MediaPipe Physical Activity Monitoring Web Application Development

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Utilizing Gyroscope and Accelerometer for Self-Managed Crystal Methamphetamine Recovery and Private Monitoring 4

Utilizing Gyroscope and Accelerometer for Self-Managed Cryst...

引用

4th international Mobile, Intelligent, and Ubiquitous Computing conference

作者： Ahmed, Ali Amr, Omar Atia, Ayman October Univ Modern Sci & Arts MSA Software Engn Dept Cairo Egypt Helwan Univ Fac Comp & Artificial Intelligence HCI Lab Giza Egypt October Univ Modern Sci & Arts MSA Fac Comp Sci Giza Egypt

ISBN: (纸本)9798350367782;9798350367775

Recovery from substance abuse is a continuous struggle as almost 60% of the addicts at any given instance in time during the recovery process experiences a relapse. the situation worsens during withdrawal, where the process, more so for those recovering from crystal methamphetamine addiction, often presents with intense symptoms that are really fatiguing. Mobile sensors offer a practical solution to continuously monitor these symptoms and provide near real-time insights into an individual's progress towards recovery. this paper explores two deep learning approaches, CNNs and LSTMs(Long Short Term Memory), to identify patterns related to withdrawal symptoms using accelerometer data. It will also address key limitations in current state-of-the-art methods, such as unbiased data collection, feature extraction, and mobile device implementation. Finally, the paper presents personalized reports designed to aid in therapy, helping individuals stay on track and reducing the likelihood of relapse.

关键词： Drug addiction recovery relapse prediction mobile sensors machine learning LSTM CNN-LSTM CONV-LSTM Human Activity recognition gyroscope data accelerometer data Flutter app real-time monitoring

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Pneumonia Image recognition Based On Deep learning 4

Pneumonia Image Recognition Based On Deep Learning

引用

4th IEEE international conference on Information Technology, Big data and Artificial Intelligence, ICIBA 2024

作者： Jiang, Zhengyu Toe, Teoh Teik University of Electronic Science and Technology of China Glasgow College Hn China Nanyang Technological University Ntu Business Ai Lab Singapore

ISBN: (纸本)9798350363586

this article introduces a comprehensive method for diagnosing pneumonia using chest X-ray (CXR) images. By using deep learning models for feature extraction and machine learning methods for classification, the effectiveness of feature extraction has been evaluated using CNN, ResNet.50, and ViT models. the performance of random forest and logistic regression classifiers based on these models were compared, conducting a comprehensive evaluation of accuracy and F1 score to determine the most effective model. this study addresses the challenges of diagnosing artificial pneumonia and emphasizes the urgency of automated solutions. In the end, the combination of ResNet.50 and logistic regression achieved higher accuracy (78.68%), higher precision (99.99%), and more ideal F1 score (0.6239). these results demonstrate that the application of deep learning in clinical diagnosis can improve the clinical efficiency and accuracy of pneumonia detection, which is beneficial for the subsequent treatment of patients. © 2024 IEEE.

关键词： Logistic regression

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Traffic Sign Detection in the Digital Era: Leveraging Convolutional Neural Networks 4

Traffic Sign Detection in the Digital Era: Leveraging Convol...

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4th IEEE international Multidisciplinary conference on Engineering Technology, IMCET 2023

作者： Kothai, G. Anbumozhi, Anna Abirami, R. Kavin, R.A. Karthikeyan, M. Kalyani, T. Gokulpandian, M. TamilNadu Coimbatore India SRM Institute of Science and Technology Department of Data Science and Business TamilNadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies TamilNadu Chennai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering TamilNadu Virudhunagar India

ISBN: (纸本)9798350313826

Road safety depends heavily on traffic signs, and as there are more vehicles on the road, the demand for precise traffic sign recognition systems rises. the development of machine-learning algorithms to predict and recognize traffic signs in real-time has been the main focus of research. An overview of the state of advancement in image analysis and classification for recognizing road signs is given in this article. It also goes through their benefits and drawbacks in various traffic situations. Managing various lighting and weather situations, the necessity for real-time processing, and future connection with autonomous driving systems are just a few of the difficulties that are highlighted in the paper. this study seeks to provide guidance to professionals working in the areas of autonomous driving, computer vision, and machine learning who recognize traffic signs. © 2023 IEEE.

关键词： Traffic signs

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Deep learning Based Intrusion Detection for IoT Networks 4

Deep Learning Based Intrusion Detection for IoT Networks

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Ananthi, P. Nirmaladevi, K. Mothika, G. Kongu Engineering College Department of Computer Technology-PG Tamilnadu Erode638060 India Madanapalle Institute of Technology & Science Department of Computer Applications India Kongu Engineering College Erode India

ISBN: (纸本)9798331529635

Intrusion detection is crucial for securing IoT networks amid the rapid proliferation of devices, posing significant security challenges. this study offers a unique intrusion detection model for IoT deep learning methods used in networks include Convolutional Neural Networks (CNN), TabNet, and the Transient Search Optimization (TSO) method. the project starts by developing and evaluating distinct intrusion detection components. CNN, specializing in feature extraction, analyzes network traffic for potential intrusions. TabNet, known for interpretability and effectiveness with structured data, captures intricate relationships. the novel TSO algorithm improves the system's ability to detect temporary anomalies. Each component's performance is assessed individually, highlighting unique strengths. TabNet excels in structured IoT data pattern identification, surpassing CNN. the TSO algorithm strengthens temporary anomaly recognition, enhancing IoT network security. An ensemble technique combines CNN and TabNet predictions, reducing false positives and negatives to elevate overall detection accuracy. the ensemble, using both CNN and TabNet, achieves an impressive accuracy rate exceeding 99%, demonstrating significant gains in intrusion detection performance. © 2024 IEEE.

关键词： Network intrusion

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