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A Residual Neural Network-Based Incremental learning Model for Driver State Perception 4

A Residual Neural Network-Based Incremental Learning Model f...

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4th international conference on Electronic Communication and Artificial Intelligence, ICECAI 2023

作者： Cai, Jie Zhang, Jingming Sun, Erxin Cai, Jianjun Company Ltd Research and Development Department Tianjin China

ISBN: (纸本)9798350314496

this paper presents a model for implementing active machine sensing of fatigue states in an intelligent cockpit. To make the model more robust and achieve higher accuracy, the model introduces residual modules in the convolutional neural network and incorporates input primitive attributes and contour information in the learning. In addition, new learning branches are added to the network. By learning the user's personalized performance data in the new branch, the model is more adaptable to different drivers. With reference to the user state by the model perceived, the cockpit switches between different cloud scenario modes. Finally, in comparative experiments, our model shows advantages over other models in driver state recognition. © 2023 IEEE.

关键词： learning systems

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4th international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2024

4th International Conference on Innovations in Computational...

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4th international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2024

ISBN: (纸本)9789819769919

the proceedings contain 40 papers. the special focus in this conference is on Innovations in Computational Intelligence and Computer Vision. the topics include: dataset Balancing Techniques and Supervised learning Algorithms for Predictive Analysis of Rice and Corn Yields;fish Blood Cell as Biological Dosimeter: In Between Measurements, Radiomics, Preprocessing, and Artificial Intelligence;predictive Analysis for Early Detection of Breast Cancer through Artificial Intelligence Algorithms;boosting Security: An Effective Approach to Intrusion Detection in Wireless Sensor Networks with AdaBoost Classifiers;Chat2Fluency: Enhancing Language learning through Conversational AI;REED-NET: Residual Enhanced Encoder-Decoder Network for Low-Dose CT Reconstruction;comparative Analysis of Large Language Models;Visualizing Insights to Empower HR Decision-Making: A data-Driven Approach;handling Uncertainty in Parkinson’s Disease Voice data Using Intuitionistic Fuzzy Entropy Measure;Exploring Shopping Opportunities and Elevating Customer Experiences through AI-Powered E-Commerce Strategies;ioT-Based Vehicle Class Detection for Smart Traffic Control;Domain Adaptation for NER Using mBERT;SQL Query Recommendation Based on Matrix Factorization;retenSure: Ensemble learning for Managing Employee Attrition;Subject–Verb Agreement Error Handling Using RNN Architectures;ad-Spend Analytics;an Efficient Real-Time Word-Level recognition of Indian Sign Language;innovating Drug Design for Alzheimer’s Disease via Reinforcement learning for Enhanced Molecular Generation;an Efficient Real-Time recognition of Static Kannada Sign Language;unveiling Diagnostic Clarity: A machine learning Approach to Distinguish Borderline Personality Disorder and Bipolar Disorder for Enhanced Mental Health Diagnostics;DLSTM with Adam Waterwheel Optimization for Groundwater Level Prediction in India;PhishGuard: machine learning Model for Real-Time URL Detection;Improved Grasshopper Optimization with Squeezenet (IGO-SNet)

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4th international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2024

4th International Conference on Innovations in Computational...

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4th international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2024

ISBN: (纸本)9789819769940

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learning to Optimize Quantum Neural Networks Without Gradients 4

Learning to Optimize Quantum Neural Networks Without Gradien...

引用

4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Kulshrestha, Ankit Liu, Xiaoyuan Ushijima-Mwesigwa, Hayato Safro, Ilya Newark United States Fujitsu Research of America Sunnyvale United States

ISBN: (纸本)9798350343236

Quantum machine learning is an emerging sub-field in machine learning where one of the goals is to perform pattern recognition tasks by encoding data into quantum states. this extension from classical to quantum domain has been made possible due to the development of hybrid quantum-classical algorithms that allow a parameterized quantum circuit to be optimized using gradient based algorithms that run on a classical computer. the similarities in training of these hybrid algorithms and classical neural networks has further led to the development of Quantum Neural Networks (QNNs). However, in the current training regime for QNNs, the gradients w.r. t objective function have to be computed on the quantum device. this computation is highly non-scalable and is affected by hardware and sampling noise present in the current generation of quantum hardware. In this paper, we propose a training algorithm that does not rely on gradient information. Specifically, we introduce a novel meta-optimization algorithm that trains a meta-optimizer network to output parameters for the quantum circuit such that the objective function is minimized. We empirically and theoretically show that we achieve a better quality minima in fewer circuit evaluations than existing gradient based algorithms on different datasets. © 2023 IEEE.

关键词： pattern recognition

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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...

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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...

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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...

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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...

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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...

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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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