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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 763 条记录，以下是191-200 订阅

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A Low-Complexity FPGA-Based Neural Network for Hand-Arm Vibrations Classification 6

A Low-Complexity FPGA-Based Neural Network for Hand-Arm Vibr...

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6th IEEE international Workshop on Metrology for Industry 4.0 and IoT, MetroInd4.0 and IoT 2023

作者： Addabbo, Tommaso Landi, Elia Moretti, Riccardo Parri, Lorenzo Peruzzi, Giacomo Pozzebon, Alessandro Spinelli, Filippo University of Siena Department of Information Engineering and Mathematics Siena Italy University of Padua Department of Information Engineering Padua Italy

ISBN: (纸本)9798350396577

this paper discusses the development of wearable devices for monitoring vibrations induced on the hand-arm system, and the challenges involved in modifying measurement procedures defined by the ISO 5349 standard to be employable on-field. the paper proposes the use of embedded Artificial Intelligence (AI) by implementing machine learning (ML) algorithms on low-complexity devices, such as microcontrollers and FPGAs, to perform inferences based on data acquired by on-board sensors. the paper presents an ML-based approach for the classification of vibrations applied on the hand-arm system, distinguishing between harmful and harmless accelerations. the paper describes the dataset used for the NN training, validation, and testing, and the input signal preprocessing performed via FFT. the paper also discusses the advantages of using FPGAs for implementing the NN model and input signal processing and presents a lighter NN model exploiting sigmoidal activation functions for equivalent performance. © 2023 IEEE.

关键词： machine learning

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Development of a Sign Language Recognition System Using machine learning 6

Development of a Sign Language Recognition System Using Mach...

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6th international conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems, icABCD 2023

作者： Orovwode, Hope Oduntan, Ibukun Deborah Abubakar, John Covenant University Electrical and Information Engineering Ogun State Ota Nigeria

ISBN: (纸本)9798350314809

Deafness and voice impairment have been persistent disabilities throughout history, hindering individuals from engaging in verbal communication and leading to their isolation from the predominantly vocally communicating society. Sign language has emerged as the primary mode of communication for people with these disabilities. However, it presents a language barrier as it is not commonly understood by those who can hear. To address this issue, various methods for recognizing sign language have been proposed. this paperaims to develop a machine learning-based system that can recognize sign language in real-time. the paper involved the acquisition of a dataset consisting of 44,654 images representing the static American Sign Language (ASL) alphabet signs. the HandDetector module was utilized to detect and capture images of the signer's hand forming each sign through a PC webcam. the dataset was split into three sets: training data (20,772 cases), validation data (8,903 cases), and test data (14,979 cases). Image pre-processing techniques were implemented on the images and a convolutional neural network (CNN) model was trained and compiled. the CNN utilized in the paper comprised of three convolutional layers and a SoftMax output layer and it was compiled using the Adam optimizer and categorical cross-entropy loss function. the performance of the system was evaluated using the test dataset. Notably, the system achieved remarkable accuracy rates, having a training accuracy of 99.86%, a validation accuracy of 99.94%, and a test accuracy of 94.68%. the results obtained from this study demonstrated significant advancements in sign language recognition, surpassing previous findings in the literature. © 2023 IEEE.

关键词： machine learning

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A Deep learning Framework for Real-Time Detection of Rotten Fruits Using Yolov5s Architecture 6th

A Deep Learning Framework for Real-Time Detection of Rotten ...

引用

6th international conference on Information Systems and Management Science, ISMS 2023

作者： Malik, Nausheen Sahu, Pankaj Jharia, Bhavana Jabalpur Engineering College Jabalpur482011 India

ISBN: (纸本)9783031707889

Maintaining food safety and quality is always a necessity, as food is a vital source of sustenance. In current times, the implementation of computer vision-based technologies is proving to be efficient for real-life implementations, creating a digital world. this research work is focused on the application of a deep learning approach in food processing areas to mitigate the problem of fruit rotting on a large scale. A CNN-based Yolov5s model is proposed for automated detection of rotten and fresh apples using a dataset of 10,228 images. For this model, the map50 and map50-90 values were found to be 0.993 and 0.985, respectively. On testing the model with real data samples, the model is capable of detecting rotten fruits among the fresh fruits with images captured in varying conditions, proving the efficacy of the model. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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6th IFIP international conference on Internet of things, IFIP IoT 2023

6th IFIP International Conference on Internet of Things, IFI...

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6th IFIP international conference on Internet of things, IFIP IoT 2023

the proceedings contain 63 papers. the special focus in this conference is on Internet of things. the topics include: Blockchain-Based Secure Noninvasive Glucometer and60;Automatic Insulin Delivery System for60;...

ISBN: (纸本)9783031458811

the proceedings contain 63 papers. the special focus in this conference is on Internet of things. the topics include: Blockchain-Based Secure Noninvasive Glucometer and Automatic Insulin Delivery System for Diabetes Management;an Efficient and Secure Mechanism for Ubiquitous Sustainable Computing System;understanding Security Challenges and Defending Access Control Models for Cloud-Based Internet of things Network;fog Computing in the Internet of things: Challenges and Opportunities;A 2-Colorable DODAG Structured Hybrid Mode of Operations Architecture for RPL Protocol to Reduce Communication Overhead;role-Based Access Control in Private Blockchain for IoT Integrated Smart Contract;VXorPUF: A Vedic Principles - Based Hybrid XOR Arbiter PUF for Robust Security in IoMT;Easy-Sec: PUF-Based Rapid and Robust Authentication Framework for the Internet of Vehicles;fortiRx: Distributed Ledger Based Verifiable and Trustworthy Electronic Prescription Sharing;a Survey of Pedestrian to Infrastructure Communication System for Pedestrian Safety: System Components and Design Challenges;survival: A Smart Way to Locate Help;federated Edge-Cloud Framework for Heart Disease Risk Prediction Using Blockchain;understanding Cybersecurity Challenges and Detection Algorithms for False Data Injection Attacks in Smart Grids;comprehensive Survey of machine learning Techniques for Detecting and Preventing Network Layer DoS Attacks;power Analysis Side-Channel Attacks on Same and Cross-Device Settings: A Survey of machine learning Techniques;Lite-Agro: Exploring Light-Duty Computing Platforms for IoAT-Edge AI in Plant Disease Identification;farmIns: Blockchain Leveraged Secure and Reliable Crop Insurance Management System;PTSD Detection Using Physiological Markers;a signal Conditioning Circuit with Integrated Bandgap Reference for Glucose Concentration Measurement;detection of Aircraft in Satellite Images using Multilayer Convolution Neural Network;computer Vision Based 3D Model Floor Construction

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6th international conference on New Trends in Information and Communications Technology Applications, NTICT 2022

6th International Conference on New Trends in Information an...

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6th international conference on New Trends in Information and Communications Technology Applications, NTICT 2022

ISBN: (纸本)9783031354410

the proceedings contain 14 papers. the special focus in this conference is on New Trends in Information and Communications Technology Applications. the topics include: A Novel Ensemble Method for Network-Based Anomaly Intrusion Detection System;New Security Mechanism of Health Data Based on Blockchain–NFV;Use Multichannel EEG-Based Biometrics Authentication signal in Real Time Using Neural Network;machine learning-Based DDoS Attack Detection in Software-Defined Networking;enhancing Service Classification for Network Slicing in 5G Using machine learning Algorithms;mesoNet3: A Deepfakes Facial Video Detection Network Based on Object Behavior Analysis;computer Vision Techniques for Hand Gesture Recognition: Survey;a Comprehensive Review in Using the Advances of Deep learning in the 3D Race Classification;multi-agents as Data Mining Classifiers and Crawlers to Get Intelligent E-Advertisements;automated Multi-style Iraqi Cars License Recognition Based on Convolutional Neural Network Technology;food Recommendation System Based on Data Clustering Techniques and User Nutrition Records;EEG Data Compression Using Tap9/7 Wavelet Transform and Double Shift-Coding.

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A Study on machine learning and Deep learning Techniques Applied in Predicting Chronic Kidney Diseases 1

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6th international conference on Soft Computing and signal processing, ICSCSP 2023

作者： Chapa, Kalyani Ravi, Bhramaramba Andhra Pradesh Visakhapatnam India

ISBN: (数字)9789819984510

ISBN: (纸本)9789819984503

Chronic kidney disease (CKD) is one of the heterogeneous disorders in which the kidneys’ functionality degenerates over time. Although there is a range of abnormalities in kidney function, the malfunction going beyond a threshold leads to untreated kidney failure, also narrated as end-stage renal disorder. However, at times, high-end complex treatments such as kidney transplantation or dialysis may also be life-threatening in CKD patients. the situation often leads to irreversible kidney structure and function, which may also implicate cardio, endocrine, and xenobiotic toxic complications. CKD is identified as a decrease in GFR and/or a rise in albuminuria. As this health disorder becomes more prevalent, the quality of life index becomes detrimental. Moreover, the consequences impact the nation’s economy direct or indirectly. At this juncture, suitable preventive measures and strategic planning are imperative. On the other hand, the world is advancing with modern innovations. Artificial Intelligence, machine learning, and Deep learning are unique technologies exhaustively employed in every sector. these disruptive technologies did not exempt the health segment and even proved their supremacy in several contexts. Accurate disease prediction and early detection are among the outcomes that could be expected from these technologies, so preventive measures could be suggested beforehand. In this article, a comprehensive investigation done by distinguished researchers is explored and presented. Around 100 articles published during the past decade are part of our study, which are deep-dived, and the respective contributions are cited. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Sugarcane Bud Detection Using YOLOv5 6th

Sugarcane Bud Detection Using YOLOv5

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6th international conference on Recent Trends in Image processing and Pattern Recognition, RTIP2R 2023

作者： Sindhe, P. Swathi Hegadi, Ravindra S. Department of Computer Science Central University of Karnataka Kadaganchi Karnataka Kalaburagi India

ISBN: (纸本)9783031530814

this paper addresses the labor-intensive and wasteful nature of the traditional sugarcane bud cutting method. To overcome these challenges, the proposed approach leverages YOLOv5 technology for sugarcane bud identification. the machine-learning model is trained with diverse data samples, enabling it to accurately distinguish between sugarcane buds and other elements in the images. the implementation yields the best of 79% accuracy in bud detection. By automating the process, the proposed method significantly reduces labor and time requirements while minimizing sugarcane wastage. this innovation presents promising implications for the sugarcane industry, as it streamlines bud identification and optimizes resource utilization. Furthermore, the adoption of YOLOv5 technology can potentially extend to other agricultural domains, offering opportunities for enhanced crop management and sustainable farming practices. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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6th IFIP international conference on Internet of things, IFIP IoT 2023

6th IFIP International Conference on Internet of Things, IFI...

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6th IFIP international conference on Internet of things, IFIP IoT 2023

ISBN: (纸本)9783031458774

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Effective Prediction of Brain Tumor Using machine learning Algorithms 6th

Effective Prediction of Brain Tumor Using Machine Learning A...

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6th international conference on Communications and Cyber-Physical Engineering, ICCCE 2023

作者： Vikkurty, Sireesha Hegde, Nagaratna P. Vinay Kumar, S. Recherla, Anishka Ganapa, Meghana Vasavi College of Engineering Telangana Hyderabad India DAZN Hyderabad India University of Melbourne Melbourne Australia

ISBN: (纸本)9789819971367

Medical science has been advancing with the help of technology in which machine learning plays a major role starting from checking a person’s vitals to predicting benign tumors in almost all parts of the body. So, these advancements have inspired us and got us interested in brain tumor prediction. Brain Tumor prediction is one of the most crucial and tedious tasks in the field of medical image processing as classification of tumors manually can result in inaccurate diagnosis and prediction. As there is much similarity between normal tissues and tumors, there is difficulty in their classification, thus making them unyielding. We have used various classifiers namely logistic regression, random forest, decision tree, and Naïve Bayes in order to predict whether a tumor is there or not. the observed accuracies for the above classifiers were not satisfactory. So, in order to predict the tumors with utmost accuracy, we have chosen a machine learning algorithm called Convolutional neural network using Keras and Tensorflow. the convolutional neural network is a deep learning technique in image classification. this is a supervised learning algorithm and feed-forward neural network with 20–30 networks used to identify patterns in raw images without any image processing. this technique has resulted in an accuracy of 90%. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tumors

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machine learning-Based Low-Complexity Image Feature Descriptor for MPEG-CDVS Standard 6

Machine Learning-Based Low-Complexity Image Feature Descript...

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6th IEEE international conference on Knowledge Innovation and Invention, ICKII 2023

作者： Jiang, You-Wei Chen, You-Rong Lin, Shih-Hsiang Chen, Pei-Yin National Cheng Kung University Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350323535

Compact descriptors for visual search (CDVS) is a completed standard from the ISO/IEC moving pictures experts group (MPEG). CDVS has a low complexity and bitrate efficiency on image matching and retrieval. the MPEG CDVS framework detects key feature points in the image and extracts descriptors to obtain bitstreams to match images. We proposed a low-complexity image descriptor for visual searching within the MPEG CDVS framework. the proposed algorithm integrated traditional image processing methods with a machince learning (ML) network, resulting in reduced compression time and the size of the CDVS standard bitstream. To evaluate the effectiveness of the proposed algorithm, we tested the proposed algorithm with the CDVS standard software to ensure its compatibility. On the CDVS-Benchmark, the experimental results demonstrated that the proposed algorithm reduced matching time by 31.7%, bitstream size by 31.8%, and extracting time by 12.8% compared to the CDVS standard test model. Similarly, on the HPatches-Benchmark, the proposed algorithm reduced matching time by 5.3%, bitstream size by 25.2%, and extracting time by 32.0% compared to the CDVS standard test model. © 2023 IEEE.

关键词： Software testing

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