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Human behaviour analysis and face detection using machine learning 4

Human behaviour analysis and face detection using machine le...

引用

4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Dohare, Anand Kumar Sharma, Megha Pathak, Ravi Shanker Greater Noida Institute of Technology Dept. of Information Technology India Gcet Dept. of Information Technology U.P. Gr. Noida India

ISBN: (纸本)9781665474368

Human behaviour analysis is a difficult aspect to maintain for a normal human being. this human behaviour analysis is unpredictable generally. the alignment of advanced technology such as machine learning is essential in this context. the machine learning technology plays an important role in structuring all human data and analysis their behavioural pattern considering accuracy and frequency of response. this research has evaluated multiple factors of human behaviour analysis considering its major of utilisation. this research has followed secondary research method based few specific and realistic objectives. An ethical consideration section states the ethical norms that this research has maintained. All the collected data of this research have been analysed considering all research objectives. © 2022 IEEE.

关键词： Face recognition

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Automatic Disease Classification of Paddy Leaf Images Using Deep learning Algorithms 4

Automatic Disease Classification of Paddy Leaf Images Using ...

引用

4th international conference on Signal Processing and Communication, ICSPC 2023

作者： Velusamy, Durgadevi Mounika, A. Ragulraj, D. Vigneshwaran, K. M.Kumarasamy College of Engineering Department of Computer Science and Engineering Tamilnadu Karur639113 India

ISBN: (纸本)9798350300772

Agriculture is considered one of the primary sectors in India. Especially rice is the primary stable food widely cultivated and consumed by 50% of the Indian population. Paddy crop is susceptible to several diseases like other crops that indirectly affect agricultural development and economic loss. To address this limitation, in this study, we have applied deep learning algorithms for the recognition and classification of diseases at an earlier stage. the paddy plant disease diagnosis is made by applying image processing techniques to identify the changes in leaves and its texture. We have used pre-processing techniques for segmentation, feature extraction and classification of disease. First, we have to perform data augmentation to improve the number of images in the dataset. Second, the segmentation of plant leaves are performed to identify the test infected region. then, we applied CNN algorithm to extract features from the data. After feature extraction, we trained different classification algorithms to recognize and classify the images into disease and non-disease images. the application of machine learning and deep learning algorithms helps in the precise location and identification of infectious leaves that help farmers to use automated methods for disease diagnosis. © 2023 IEEE.

关键词： Feature extraction

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Online Document Identification and Verification Using machine learning Model 4th

Online Document Identification and Verification Using Machin...

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4th international conference on data, Engineering, and Applications, IDEA 2022

作者： thalange, A.V. Shetgar, P.S. Patnaikuni, Dinkar Chamatagoudar, S.N. Walchand Institute of Technology Solapur India

ISBN: (纸本)9789819700363

In companies or any organizations, the data is collected in various formats along with government-approved identity documents such as Aadhaar, PAN, license, etc. in soft or hard copy format. they contain valuable information but are stored in an unstructured manner once received. the data on the document needs to be extracted and classified and identity documents need to be verified. Nowadays, many document classifier software products are available in the market for extracting text information and classifying documents. However, these products are used in offline mode, once the documents are collected at the receiver end. this paper proposes a novel method for classifying and verifying identity documents in run time while uploading the document online mode itself. For this, support vector machine (SVM) algorithm and optical character recognition (OCR) are used. For invalid documents, the comment is displayed on the screen to upload the correct document, before submitting data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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A 3D-CLDNN Based Multiple data Fusion Framework for Finger Gesture recognition in Human-Robot Interaction 4

A 3D-CLDNN Based Multiple Data Fusion Framework for Finger G...

引用

4th international conference on Control and Robotics (ICCR)

作者： Qi, Wen Fan, Haoyu Xu, Yancai Su, Hang Aliverti, Andrea South China Univ Technol Sch Future Technol Guangzhou Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China Politecn Milan Dept Elect Informat & Bioengn Milan Italy

ISBN: (纸本)9781665486415

Finger gesture recognition using surface electromyography (sEMG) became an efficient Human-Robot Interaction (HRI) solution. Although machine learning (ML) techniques are widely applied in this field, the general solutions for labeling and collecting big datasets impose time-consuming implementation and heavy workloads. In this paper, a new deep learning structure, namely three-dimensional convolutional long short-term memory neural networks (3D-CLDNN) for finger gesture identification based on depth vision and sEMG signals, was proposed for human-machine interaction. It automatically labels the depth data by the self-organizing map (SOM) and predicts the hand gesture only adopting sEMG signals. the 3D-CLDNN method is integrated to improve the recognition rate and computational speed. the results showed the highest clustering accuracy (98.60%) and highest accuracy (84.40%) with the lowest computational time compared with different approaches. Finally, real-time human-machine interaction experiments are performed to demonstrate its efficiency.

关键词： Multimodal data fusion Human-robot interaction Deep learning Signal processing

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Research and Application of Commodity recognition Algorithm Based on machine learning 4

Research and Application of Commodity Recognition Algorithm ...

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4th international conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Siyu, Du Chee Hooi, Steve Teoh Ray, Sayan Kumar Wei, Goh Wei Gupta, Sarita School of Computer Science Taylor's University Subang Jaya Malaysia Lovely Professional University Division of Research and Development Punjab Phagwara144411 India KKR and KSR Institute of Technology and Sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

With the development of society, the traditional retail industry has evolved into a pattern of coexistence of chain supermarkets, e-commerce sales, and unmanned retail, which greatly facilitates people's lives. However, the traditional retail industry faces many challenges in the management and classification of goods. One of the main problems is that the identification and classification of goods becomes cumbersome and takes a long time to operate, resulting in inefficiency. As the retail scale continues to expand and the variety of goods continues to increase, the problem becomes more prominent. therefore, this research aims to develop a commodity identification and classification system to solve the problems that may be faced in real life. the research will employ CNN (CNN) and You Only Look Once (YOLO) algorithms to develop the above system. the data of the products come from the open-source datasets of the Internet, which are from the Products-10K and RP2K datasets of the Kaggle competition. On the other hand, the research also aims to make more attempts to optimize the model and algorithm and strive to obtain quasi-excellent parameter values. Finally, the significance of this research research is to develop a more intelligent, efficient, and accurate product identification technology, which can automatically process product data and sales data, and provide consumers with a more convenient and personalized shopping experience. © 2024 IEEE.

关键词： Sales

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Auto-commissioning Technology of Engineering Equipment Driven by machine learning 4

Auto-commissioning Technology of Engineering Equipment Drive...

引用

4th international conference on Informatics Engineering and Information Science (ICIEIS)

作者： Rao, Youfu Liu, Yi Zou, Guotong Zhang, Zuozhi Wang, Yun Yao, Zhiheng SANY Automobile Mfg Co Ltd Changsha 410000 Peoples R China

ISBN: (数字)9781510651999

ISBN: (纸本)9781510651999;9781510651982

the auto-commissioning system of engineering equipment requires real-time identification of the working mode with the whole product. Determine whether there is any abnormality in the commissioning process according to the parameter standards of each working mode, to evaluate the compliance rate of the commissioning. In this article, the working mode of engineering equipment is determined by Toeplitz Inverse Covariance-based Clustering (TICC) and the time series data are segmented and clustered. Each mode in the TICC is defined by a Markov Random Field (MRF), which characterizes the interdependence between different factors in the mode. On this basis, highlight the characteristics of the model and the importance of each factor. By using the TICC method to commissioning the concrete pump truck, the results of automatic identification show that the method has high accuracy in the recognition of the working mode of engineering equipment. the successful application of the online commissioning and monitoring system of the whole machine based on this technology provides a new idea for the development of the industry.

关键词： Multivariate analyses pattern recognition time-series clustering machine commissioning

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A Framework for Daily Living Activity recognition using Fusion of Smartphone Inertial Sensors data 4

A Framework for Daily Living Activity Recognition using Fusi...

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4th international conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Khan, Sheharyar Shah, Syed M. Adnan Noorani, Sadam Hussain Arsalan, Aamir Ehatisham-Ul-Haq, M. Raheel, Aasim Ahmed, Wakeel University of Engineering and Technology Taxila Pakistan Fatima Jinnah Women University Rawalpindi Pakistan Air University Islamabad Pakistan

ISBN: (纸本)9798350335316

Recent years have seen rapid advancements in the human activity recognition field using data from smart sensor devices. A wide variety of real-world applications can be found in different domains, particularly health and security. Smartphones are common devices that let people do a wide range of everyday tasks anytime, anywhere. the sensors and networking capabilities found in modern smartphones enable context awareness for a wide range of applications. this research mainly focuses on recognizing human activities in the wild for which we selected an in-the-wild extra-sensory dataset. Six human activities i.e., lying down, sitting, standing, running, walking, and bicycling are selected. Time domain features are extracted and human activity recognition is performed using three different machine learning classifiers i.e., random forest, k-nearest neighbors, and decision trees. the proposed human activity recognition scheme resulted in the highest classification accuracy of 89.98%, using the random forest classifier. Our proposed scheme outperforms the state-of-the-art human activity recognition schemes in the wild. © 2023 IEEE.

关键词： Smartphones

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Integration of Vision IoT, AI-based OCR and Blockchain Ledger for Immutable Tracking of Vehicle's Departure and Arrival Times 23

Integration of Vision IoT, AI-based OCR and Blockchain Ledge...

引用

8th international conference on machine learning Technologies (ICMLT)

作者： Sichinga, Marumbo Nizeyimana, Eric Gatare, Ignace Nsenga, Jimmy Univ Rwanda African Ctr Excellence Internet Things ACEIoT Kigali Rwanda

ISBN: (纸本)9781450398329

Vehicle based logistics are hinged on their ability to timely deliver goods, services, and people. the classical expression of "time is money" comes alive in the logistics industry yielding potentially huge financial and health consequences in case of missing deadlines. this is especially the case for time sensitive pharmaceuticals, delivery of perishable goods, delivery of people travelling, delivery of services in fault fixing/recovery sector. All these use cases motivate the need for an immutable, secure, and immortalized process of tracking time. To solve this challenge, this paper presents prototype-based research that integrates the 4th industrial revolution technologies of vision Internet of things (IoT), Artificial Intelligence (AI)-based Optical Character recognition (OCR) and blockchain. the developed prototype features a Raspberry-PI board embedding a camera, an Artificial Intelligence (AI) model to recognize plate letters from the image and a crypto wallet to sign the logging of plate number and time events on the NEAR blockchain, an emerging sharded, proof-of-stake, layer-one blockchain that is simple to use, secure and scalable. the effective operation of the developed prototype has been validated inside a campus parking and shows an accuracy of 80%. the benefits of transparency, security, and immutability of the blockchain combined with the intelligence, data capture, and processing of IoT will enable to develop accountability solutions trusted by all different logistic stakeholders.

关键词： Web3 Blockchain Dapp NEAR IoT YoloV4 OCR

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Research on Image Classification Based on Extreme learning machine and Convolutional Neural Network 4

Research on Image Classification Based on Extreme Learning M...

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4th international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Sun, ChangXu Zhang, Rui Shenyang Jianzhu University China

ISBN: (纸本)9781510666412

In image classification and recognition, in order to obtain higher classification accuracy, it is necessary to extract more accurate and more expressive features of image semantic information[1]. this paper proposes an improved image classification model based on convolutional neural network. By improving and optimizing AlexNet from both the network architecture and the internal structure, we can further improve the expression ability of features. By introducing the extreme learning machine in the full connection layer, not only the classification ability and classification time of the network are improved, but also the structure has better data processing ability. © 2023 SPIE.

关键词： Image classification

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4th international conference on machine learning for Cyber Security, ML4CS 2022

4th International Conference on Machine Learning for Cyber S...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

ISBN: (纸本)9783031200953

the proceedings contain 152 papers. the special focus in this conference is on machine learning for Cyber Security. the topics include: machine learning Based Security Situation Awareness Method for Network data Transmission Process;multi-objective Hydrologic Cycle Optimization for Integrated Container Terminal Scheduling Problem;High Voltage Power Communication Network Security Early Warning and Monitoring System Based on HMAC Algorithm;Large Scale Network Intrusion Detection Model Based on FS Feature Selection;Privacy Preserving CSI Fingerprint Device-Free Localization;Research on Intelligent Detection Method of Automotive Network data Security Based on FlexRay/CAN Gateway;adversarial Attack and Defense on Natural Language Processing in Deep learning: A Survey and Perspective;a Novel Security Scheme for Mobile Healthcare in Digital Twin;Construction of Security Risk Prediction Model for Wireless Transmission of Multi Axis NC Machining data;spiking Neural Networks Subject to Adversarial Attacks in Spiking Domain;Diverse Web APIs Recommendation with Privacy-preservation for Mashup Development;network Security Evaluation Method of College Freshmen Career Counseling Service Based on machine learning;FedTD: Efficiently Share Telemedicine data with Federated Distillation learning;increase Channel Attention Based on Unet++ Architecture for Medical Images;distributed Power Load Missing Value Forecasting with Privacy Protection;A Novel Blockchain-MEC-Based Near-Domain Medical Resource Sharing Model;differentially Private Generative Model with Ratio-Based Gradient Clipping;differential Privacy Protection Algorithm for data Clustering Center;improved Kmeans Algorithm Based on Privacy Protection;symmetry Structured Analysis Sparse Coding for Key Frame Extraction;data Reconstruction from Gradient Updates in Federated learning;natural Backdoor Attacks on Speech recognition Models;boarding Pass Positioning with Jointly Multi-channel Segmentation and Perspective Transformation Co

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