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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291-300 订阅

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Image Based Classification of Methods-Time Measurement Operations in Assembly Using Recurrent Neuronal Networks 6th

Image Based Classification of Methods-Time Measurement Opera...

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6th international conference on System-Integrated Intelligence (SysInt)

作者： Rueckert, Patrick Birgy, Katrin Tracht, Kirsten Univ Bremen Bremen Inst Mech Engn BIME Badgasteiner Str 1 D-28359 Bremen Germany

ISBN: (纸本)9783031162817;9783031162800

Image based classification enables the acquisition and transfer of data from manual assembly workstations into a digital environment. Based on the Methods-Time Measurement method, assembly processes are transformed into short, discrete basic operations that are recognised by means of image processing and used as input data for a multilayer neural network. A recurrent neural network algorithm is investigated for its applicability in combination with the sensor data. the five basic MTM operations reaching, grasping, bringing, releasing, and positioning are classified and additional influencing factors, as well as the implementation of an object recognition, are investigated. the following paper addresses the question of the extent to which manual assembly processes can be reliably derived from visual sensor data and classified by machine learning algorithms.

关键词： Neuronal networks Assembly Image processing

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ARTIFICIAL INTELLIGENCE-BASED DESIGN OF PROCESS PARAMETERS IN LASER POWDER BED FUSION OF TI-6AL-4V FOR DESIRED SOLIDIFICATION STRUCTURE 18

ARTIFICIAL INTELLIGENCE-BASED DESIGN OF PROCESS PARAMETERS I...

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ASME 18th Int Mfg Sci and Engn Conf held jointly with the 51st N Amer Res Conf / Japan-Soc-of-Mech-Engineers Int Conf on Leading Edge Mfg/Mat and Proc

作者： Vaghefi, Ehsan Mirkoohi, Elham Auburn Univ Dept Mech Engn Auburn AL 36849 USA

ISBN: (纸本)9780791887233

Metallic additive manufacturing (AM) technologies can be utilized to manufacture components with tailored and desired microstructure and properties. However, this is just a promise. Fast and accurate computational methodologies are required to optimize the process parameters to achieve the desired microstructure and eliminate the trial-and-error process optimization methodologies. In this paper, a surrogate machine learning model is implemented to design the additive manufacturing (AM) process parameters, including scan speed, laser power, and scanning strategies, to achieve desired microstructure during AM of Ti-6Al-4V. Temperature gradient and solidification rate are the factors that control the target solidification structure, i.e., columnar to equiaxed transition (CET) of grains, to achieve desired mechanical properties. A multi-track finite element model is established and validated to simulate the temperature field during AM process using different scan strategies within a layer. As a result of this process simulation, a dataset containing temperature gradient (G), maximum temperature (T-max), and solidification rate (R) is extracted from a collection of temperature distribution graphs by using an automated signal processing technique. Next, various machine learning methods (e.g., Boosting methods, multilayer perceptron, support vector regression, and random forest) are evaluated to design process parameters through a cross-validation procedure. the surrogate model is then validated against experimentally obtained microstructures during the solidification path.

关键词： Additive manufacturing Finite element modeling machine learning Ti-6Al-4V Process parameters Grain structures

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Wireless EEG based Anxiety Screening Among Young Adults using machine learning Model 8

Wireless EEG based Anxiety Screening Among Young Adults usin...

引用

8th international conference on Biomedical Imaging, signal processing, ICBSP 2023

作者： Sakib, Nazmus Faruk, Tasnuva Islam, Md Kafiul Department of Electrical & Electronic Engineering Independent University Bangladesh Dept. of Public Health Independent University Bangladesh

ISBN: (纸本)9798400716584

Anxiety is a mental disorder that leads to palpitation, chest pain, behavioral abnormalities, etc. there are 301 million sufferers of anxiety disorders worldwide. Anxiety must be treated properly thus anxiety screening should be done more efficiently. EEG can detect abnormalities in the brain caused due to anxiety disorder. So, it can be used for screening anxiety. It may be able to overcome the drawbacks of conventional screening techniques. A machine learning-based method for screening anxiety among university-going students using wireless EEG is proposed in this study. EEG was recorded using a wireless 14-channel EEG headset from 40 students aged between 18-25 years. After using GAD-7 for screening the participants the EEG data of the participants was divided into anxiety group and anxiety control group. the data was filtered into 6 frequency bands. After extracting some nonlinear features, the SVM classifier with 10-fold cross-validation was used. Among all the bands the beta band (12-30Hz) had the highest accuracy of 94.88% with the Precision of 94.4%, NPV of 95.6%, Sensitivity of 97.2%, Specificity of 94.1%, and 0.96 F1 score. © 2023 ACM.

关键词： Electroencephalography

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Development of a machine learning Based Decision Support System for Rice Fungal Disease Management 6

Development of a Machine Learning Based Decision Support Sys...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Sharma, Richa Surbhi Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350304480

Rice is one of the most important grains, providing the main food for more than half of the economy's new people. However, rice production has been severely damaged by fungal diseases. In this survey, we propose a state-of-the-art device to localize fungal diseases in rice flora using photo manipulation techniques. the machine can detect and classify five new fungal diseases: brown, scab, wheat blast, blight and leaf spot. the system uses information on new images of modern wheat with and without fungal diseases. We extract features from snapshots using various imaging techniques and then use the extracted features to train a fashion algorithm. It has been observed that the proposed method provides 95% accuracy in detecting and isolating fungal diseases in rice plants. © 2023 IEEE.

关键词： aid vector device (SVM) choice tree essential issue analysis (PCA) feature extraction Fungal blast linear discriminant analysis (LDA) logistic regression Naive Bayes picture acquisition pre-processing random wooded area system state-of-the-art

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Mathematical Model (MM) and signal processing (SP) in Computer Simulation (CS) on Account of Improved Genetic Algorithm (IGA) 6th

Mathematical Model (MM) and Signal Processing (SP) in Comput...

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6th international conference on Innovative Computing, IC 2023

作者： Wang, Bo School of Science Dalian Jiaotong University Liaoning Dalian116028 China

ISBN: (纸本)9789819920914

MM and SP a computational feature in which a computer can simulate the state of a system at high speed and accurately while eliminating machine faults. Over the years, computer MM and SP have slowly been accepted and accepted by most people. Computers should consider all aspects of how to lay out MM and SP modules. Whether this system layout can meet the application requirements can be tested to prove. In this text, a CS patterning and SP module are set up, and computer MM and SP applications are processed. this text studies the MM and SP in CS on account of IGA, introduces the professional knowledge of MM and signal in CS, and expounds its operation principle. the data test shows that the MM and SP in CS on account of IGA can improve the accuracy of MM and signal efficiently. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithms

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Halloween Structured Microstrip MIMO Radiator at 5G sub-6GHz and mm-wave Frequencies 2

Halloween Structured Microstrip MIMO Radiator at 5G sub-6GHz...

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2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and signal processing, PCEMS 2023

作者： Raju, K. Naga Kavitha, A. Kaitepalli, Ch Sekara Rao Anna University Department of Information and Communication Engineering Tamil Nadu Chennai India M.Kumarasamy College of Engineering Department of Electronics and Communication Engineering Tamilnadu Karur India

ISBN: (纸本)9798350310719

the 5th generation mobile technology standards have the guts to deliver high data rates, low latency communications, and massive device connectivity. One of the challenge in front of 5G was spectrum usage, this article was presented, different frequency bands allocated to 5G in the range of sub-6GHz and millimeter wave frequencies in various countries and for the efficient distribution of the 5G systems and in order to meet the challenge of spectrum usage, developed a dual wideband three elements MIMO radiating structure capable to operate in sub-6GHz and mm-wave bands. Primarily the single element radiator was designed on 6.4 X 6.4 X 0.25 mm3 dimensioned Rogers/RT duroid substrate with Halloween structure incorporated patch and grounded with wide rectangular slot. the incorporation of Halloween structure was given gain enhancement and wide rectangular slot on ground was given bandwidth enhancement. the single element radiator was resonated in the band 25.18-29.39GHz with the gain of 4. 69dBi. the MIMO antenna structure was developed by extending the dimensions to S.4 X 23. SX0.25mm3, it was capable to operate in dual bands 5.12-5.97GHz and 12.83-40.4GHz with respect to the -10dB return losses and less than -10dB isolation was observed with respect to adjacent radiators. the Maximum gain obtained over the lower band was 6. 7dBi and higher band was 17. 36dBi. the results demonstrated that the presented antenna structure is well suitable for 5G Applications. © 2023 IEEE.

关键词： Millimeter waves

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Computer Vision Aided machine learning Framework for Detection and Analysis of Arm Flapping Stereotypic Behavior Exhibited by the Autistic Child 1

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Proceedings of the 6th IFIP TC 12 international conference on Computational Intelligence in Data Science, ICCIDS 2023

作者： Dundi, Umamaheswara Reddy Kanaparthi, V. Phani Kumar Bandaru, Ramakrishna Umaiorubagam, Ganapathy Sankar Diagnostics Therapeutics and Assistive Devices Laboratory Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India SRM College of Occupational Therapy SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India

ISBN: (数字)9783031382963

ISBN: (纸本)9783031382956

Stereotypic movements such as arm flapping, spinning, and body rocking are found to be unique characteristics in the majority of autistic children. Detection and analysis of the above said stereotypic movements at an early stage could potentially help in treating autistic children before they develop severe and socially unacceptable behaviors. Taking advantage of computer vision and media pipe machine learning framework, a threshold-based data peak filtering algorithm for detection of one of the stereotypic behavior, i.e., arm flapping is proposed in this study. Flapping behavior of autistic children, such as Arm flapping intensity and its frequency, is extracted to assess the severity of autism. this crucial information helps the therapist to analyse and estimate the variations in a child’s stereotypic behavioral pattern for Autism Spectrum Disorder (ASD) therapy. © 2023, IFIP international Federation for Information processing.

关键词： Computer vision

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MS-Former: Multi-Scale Self-Guided Transformer for Medical Image Segmentation 6

MS-Former: Multi-Scale Self-Guided Transformer for Medical I...

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6th international conference on Medical Imaging with Deep learning (MIDL)

作者： Karimijafarbigloo, Sanaz Azad, Reza Kazerouni, Amirhossein Merhof, Dorit Univ Regensburg Fac Informat & Data Sci Regensburg Germany Rhein Westfal TH Aachen Inst Imaging & Comp Vis Aachen Germany Iran Univ Sci & Technol Sch Elect Engn Tehran Iran Fraunhofer Inst Digital Med MEVIS Bremen Germany

Multi-scale representations have proven to be a powerful tool since they can take into account both the fine-grained details of objects in an image as well as the broader context. Inspired by this, we propose a novel dual-branch transformer network that operates on two different scales to encode global contextual dependencies while preserving local information. To learn in a self-supervised fashion, our approach considers the semantic dependency that exists between different scales to generate a supervisory signal for inter-scale consistency and also imposes a spatial stability loss within the scale for self-supervised content clustering. While intra-scale and inter-scale consistency losses aim to increase features similarly within the cluster, we propose to include a cross-entropy loss function on top of the clustering score map to effectively model each cluster distribution and increase the decision boundary between clusters. Iteratively our algorithm learns to assign each pixel to a semantically related cluster to produce the segmentation map. Extensive experiments on skin lesion and lung segmentation datasets show the superiority of our method compared to the state-of-the-art (SOTA) approaches. the implementation code is publicly available at Github.

关键词： Transformer Inter-scale Intra-scale Segmentation Medical Image

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Data Pre-processing Solution Using Statistical and Data Mining Techniques 6th

Data Pre-processing Solution Using Statistical and Data Mini...

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

作者： Deshkar, Prarthana A. Laghate, Koustubh Ghorpade, Aditya Padole, Divyam Shende, Harsh Kawale, Piyush Sakhare, Piyush Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur India Incredo Technologies Nagpur India Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (纸本)9783031707889

the research explores comprehensive strategies for handling missing data, encompassing statistical imputation methods and the K-Nearest Neighbors (KNN) algorithm. Various statistical techniques, such as mean, median, mode imputation, and machine learning-based approaches, are evaluated, emphasizing their impact on data quality. Additionally, the study delves into outlier detection using K-means clustering, contributing insights into the identification and handling of anomalies. the proposed utility integrates user-friendly interfaces, facilitating effective data preprocessing and analysis. the KNN algorithm, with Euclidean and NaN Euclidean distance metrics, is employed for missing value imputation, enhancing dataset completeness. the investigation concludes with considerations on the significance of Root Mean Squared Error in evaluating model performance. Overall, the research addresses critical aspects of data preprocessing, offering practical insights for researchers and practitioners. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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Optimizing Real- Time threat Detection with Feature Engineering Strategies for Intelligent NIDS 6

Optimizing Real- Time Threat Detection with Feature Engineer...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Verma, Jyoti Sethi, Monika Snehi, Manish Sharma, Ishu Kaushik, Keshav Punjabi University Department of Computer Science and Engineering Punjab Patiala India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science University of Petroleum and Energy Studies Dehradun India

ISBN: (纸本)9798350304480

the escalation of cloud-based network attacks has been observed to be in tandem with the rapid proliferation of cloud-based services worldwide. It has highlighted current network intrusion detection systems'(NIDS) inadequacies in adequately safeguarding against distributed security incidents, particularly within the framework of Internet of things (loT) networks. the significance of intrusion detection systems in safeguarding computer security cannot be overstated, and considerable research endeavours have been directed toward enhancing NIDS methodologies to counteract network interruptions. learning-based methods to improve detection accuracy by eliminating correlated, recurring, and irrelevant features are a significant area of emphasis in advancing NIDS. Prior studies have investigated the integration of machine learning (ML), Dimensionality Reduction (DR), and Deep learning (DL) methodologies to enhance the accuracy of classification outcomes on Network Intrusion Detection System (NIDS) datasets. the variability in selected features, design, and kinds of attacks among NIDS datasets requires thoroughly investigating these techniques across diverse datasets. the objective of this article is to furnish an overview of prevailing frameworks through the utilization of benchmark datasets to detect atypical attacks and obtain a comprehensive understanding of the present status of Network Intrusion Detection Systems. the efficacy of current Network Intrusion Detection Systems is assessed by categorizing feature characteristics and applying diverse machine learning methodologies. the study delves into the topic of feature selection techniques as a means of augmenting anomaly detection. Additionally, they investigate the pre-processing of Internet traffic data. Furthermore, the article explores the potential of conceptualizing virtualized real-time Intelligent NIDS using feature engineering techniques. this paper enhances comprehension of NIDS by consolidating varied resea

关键词： Deep learning Dimensionality reduction Feature Engineering Feature Selection machine learning

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