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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131-140 订阅

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machine learning based Epileptic Seizure Detection from EEG signal: Bangladesh Perspective 6

Machine Learning based Epileptic Seizure Detection from EEG ...

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6th international conference on Electrical Information and Communication Technology, EICT 2023

作者： Das, Shupta Mumu, Suraiya Akter Salam, Abdus Akhand, M.A.H. Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna9203 Bangladesh Shaheed Sheikh Abu Naser Specialized Hospital & Prince Hospital Khulna Bangladesh

ISBN: (纸本)9798350370836

Epileptic seizures (ES) pose significant challenges to individuals' well-being and quality of life, with a global impact affecting millions of people. Detecting and classifying ES is essential for proper medical intervention and management. this study addresses ES detection using EEG analysis, focusing specifically on the context of Bangladesh. this study presents a EEG-based method for detecting epileptic seizures differentiating between seizure and non-seizure signals. Utilizing empirical mode decomposition, essential subcomponents are extracted from EEG signals, from which three distinct features were derived. Neural Network (NN), the well-known machine learning model, is employed for ES classification from the extracted features. the significance of this research lies in the utilization of real-life EEG data from a hospital in Khulna, Bangladesh, rather than relying solely on publicly available benchmark datasets. processing real-life EEG data presents unique challenges, including data preprocessing and variability in EEG signals due to different devices and channel configurations. the proposed model achieved remarkable average accuracies, with 91.05% using 50 hidden neurons in the NN. To validate the effectiveness of the proposed method, outcomes were also compared with publicly available benchmark dataset. the proposed method offers a reliable and efficient approach for ES detection, particularly relevant for real-life healthcare settings in Bangladesh, contributing to improved epilepsy management. © 2023 IEEE.

关键词： Empirical mode decomposition

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Denoised Attention Neural Network for Direction-of-Arrival Estimation in Low SNR 6

Denoised Attention Neural Network for Direction-of-Arrival E...

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6th international conference on Information Communication and signal processing, ICICSP 2023

作者： Yin, Zhiqiang Zhang, Han Du, Zhaohui Wang, Xinlin School of Construction Machinery Chang'an University Xi'an China School of Marine Science and Technology Northwestern Poly Technical University Xi'an China

ISBN: (纸本)9798350339994

Direction-of-arrival (DOA) estimation methods are divided into model-driven and data-driven algorithms according to the driving method. However, traditional model-driven algorithms suffer from poor accuracy at low signal-to-noise ratios (SNR), while data-driven models fail to consider the significance between data, leading to a high number of algorithm parameters, inaccurate DOA estimation. To address these issues, we propose the Denoised Attention Neural Network (DANN), which utilizes the channel attention module, spatial attention module, and multi-head attention module to assign weights to features and learn their importance adaptively. In addition, to reduce the interference of noise on the estimation results, a threshold denoising module is introduced to achieve noise abatement. Simulation results demonstrate that our method achieves higher estimation accuracy at low SNR with smaller network parameters compared to existing methods. © 2023 IEEE.

关键词： array signal processing attention mechanism Deep learning (DL) Direction-of-Arrival (DOA) estimation multi-label classification threshold denoising

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Intelligent One Step Authentication Using machine learning Model for Secure Interaction with Electronic Devices 6th

Intelligent One Step Authentication Using Machine Learning M...

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

作者： Gelli, tharuni Mandala, Rajesh Gouri, Challa Sri Subba Rao, S.P.V. Ajitha, D. Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad India Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819984503

With the enhanced technology every process is being made very simple, especially human interaction with devices is being constantly changed. Many technologies like artificial intelligence, machine learning, blockchain, and deep learning algorithms are helping us in developing secured interactions with the devices. they are also automating the process of authentication with the help of touch less technologies. through this paper a methodology is proposed which is user friendly to illiterates and which can be proved efficient to overcome the challenges faced by the people because of the least digital literacy. this not only provides a solution for the present but can also help our future generations in handling complex transactions in a simpler manner. In the proposed methodology, real-time machine learning model is implemented which will make the process of one step authentication secure and reliable. With the increase in IoT technologies in future the number of devices interacting with each other will be very high. In that situation this model provides the best solution in terms of implementation as well as simplicity. the results obtained during real-time implementation are presented in this paper. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Authentication

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6th international conference on Information and Knowledge Systems, ICIKS 2023

6th International Conference on Information and Knowledge Sy...

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6th international conference on Information and Knowledge Systems, ICIKS 2023

ISBN: (纸本)9783031516634

the proceedings contain 24 papers. the special focus in this conference is on Information and Knowledge Systems. the topics include: Exploiting machine learning Technique for Attack Detection in Intrusion Detection System (IDS) Based on Protocol;robust Aggregation Function in Federated learning;should I Share or Should I Go?: A Study of Tacit Knowledge Sharing Behaviors in Extended Enterprises;designing a User Contextual Profile Ontology: A Focus on the Vehicle Sales Domain;Management of Implicit Ontology Changes Generated by Non-conservative JSON Instance Updates in the τJOWL Environment;a Model Driven Architecture Approach for Implementing Sensitive Business Processes;Extension of the Functional Dimension of BPMN Based on MDA Approach for Sensitive Business Processes Execution;inclusive Mobile Health System for Yoruba Race in Nigeria;epistemology for Cyber Security: A Controlled Natural Language Approach;decision and Information Support System for a Framework to Building Multicriteria Decision Models;Application of Fuzzy Decision Support Systems in IT Industry Functioning;moving Towards Explainable Artificial Intelligence Using Fuzzy Rule-Based Networks in Decision-Making Process;Sentiment Analysis: Effect of Combining BERT as an Embedding Technique with CNN Model for Tunisian Dialect;an Enhanced machine learning-Based Analysis of Teaching and learning Process for Higher Education System;topic Modelling of Legal Texts Using Bidirectional Encoder Representations from Sentence Transformers;graph Representation learning for Recommendation Systems: A Short Review;FITradeoff Decision Support System Applied to Solve a Supplier Selection Problem;a Step-By-Step Decision Process for Application Migration to Cloud-Native Architecture;ACTIVE SMOTE for Imbalanced Medical Data Classification;Evolutionary Graph-Clustering vs Evolutionary Cluster-Detection Approaches for Community Identification in PPI Networks;predictive Monitoring of Business Process Execution Delays.

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Camera and Voice Control-Based Human–Computer Interaction Using machine learning 1

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

作者： Sathwik, Ch. Harsha Vardhan, Ch. Abhiram, B. Shaik, Subhani RaviKumar, A. Telangana Hyderabad India

ISBN: (数字)9789819984510

ISBN: (纸本)9789819984503

Human–computer interaction comes with various hardware implementations out of which is mouse. Latest mouses took their form into wireless with the help of Bluetooth technology or with the help of radio frequencies, which are connected by the receiver, and mouse is powered by batteries. In the proposed method, it uses OpenCV, ML and Python and fingertips controlled and audio cues. these restrictions will be removed by adding a webcam or built-in camera enabling computer vision-based hand movements and fingertip detection. the machine learning algorithm is employed in the system's algorithm, which is the computer can be virtually controlled with hand movements to do various mouse features such as open folder, close folder, refresh, move up, move down along with cursor movements with the help of hand. Deep learning is used to track the hand movements. Hence, the put forward method will be able to make use of the computer's ability to perform tasks such that with the help of audio cues we will be able to command PC and also protect ourselves from infectious diseases such as Covid-19. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Human computer interaction

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sEMG signal-Based Prosthetic Hand Control Using Embedded machine learning with Deep Neural Network 6

sEMG Signal-Based Prosthetic Hand Control Using Embedded Mac...

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6th international conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024

作者： Rafiq, Ahsan Talukder, Zidan Hamim, Asgar Firdous, Jannatul Uddin Ahmed, Khawza Iftekhar United International University Dhaka Bangladesh Green University of Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798350385779

this study focuses on creating a prosthetic arm by leveraging surface electromyography (sEMG) signals for controlling the prosthetic arm. this novel approach holds promise for enhancing the quality of life for amputees and signifies a step forward in the field of prosthetic technology. the key contribution of this research lies in the integration of sEMG data collected from five sensors placed on the forearm muscles, the development of a realtime Deep Neural Network (DNN) model, and the installation of the DNN model on a system-on-chip (SoC) which is Arduino BLE 33 and use of multithreaded operation to recognize different hand gestures in realtime. Various gestures were performed and recorded, forming the basis for training a compact machine learning model using Edge Impulse. the resulting model was optimized for deployment on the Arduino Nano 33 BLE Sense, harnessing its machine-learning capabilities. this model eliminates the need for an internet connection or external computation. the developed prosthetic arm's control mechanism was tested against existing methods, demonstrating its ability to interpret user intentions accurately. the system's responsiveness and gesture recognition accuracy is 97.2% with a processing time of 1 ms and a max latency of 100ms which outperformed traditional approaches, marking a significant advancement in prosthesis control as well as affordable. the study successfully pioneers a specialized prosthetic arm catering to amputees lacking wrist functionality. By harnessing sEMG signals and embedded machine learning with Deep Nural Network (DNN), the developed system offers improved accuracy and responsiveness in interpreting user gestures. this innovation holds promise for enhancing the quality of life for amputees and signifies a step forward in the field of prosthetic technology. © 2024 IEEE.

关键词： Deep neural networks

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Predictive Monitoring of Business Process Execution Delays 6th

Predictive Monitoring of Business Process Execution Delays

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6th international conference on Information and Knowledge Systems (ICIKS)

作者： Ben Fradj, Walid Turki, Mohamed Univ Sfax MIRACL Lab ISIMS POB 242 Sfax 3021 Tunisia

ISBN: (纸本)9783031516634;9783031516641

Nowadays, organizations are becoming increasingly aware of the importance of better utilizing their knowledge assets and adopting a quality management model based on a process approach. this can be achieved by adopting a multidisciplinary approach that combines the fields of Knowledge Management, Business Process Management and Process Mining. therefore, to improve their performance and increase their responsiveness, organizations must identify, manage and monitor all the business processes (BP) that are likely to mobilize crucial knowledge. In fact, implementing an IT system that automates business processes is necessary to achieve these goals. In this context, we propose a new method for predicting the execution times of business processes baptized BPETPM. this method is based on the CRISP-DM approach. We used a Process Mining techniques particularly the machine learning to exploit the execution data of a workflow engine. In order to prove the applicability of this method we have developed an intelligent system for predicting execution time of BP named iBPMS4PET. the applicative framework of this research work is the incoming mail management process as part of a group health insurance.

关键词： Business Process Management Process Mining Knowledge Management machine learning CRISP-DM

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6th international conference on Dynamics of Information Systems, DIS 2023

6th International Conference on Dynamics of Information Syst...

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6th international conference on Dynamics of Information Systems, DIS 2023

ISBN: (纸本)9783031503191

the proceedings contain 18 papers. the special focus in this conference is on Dynamics of Information Systems. the topics include: Dispersion of Personal Spaces;an Evolutionary Approach to Automated Class-Specific Data Augmentation for Image Classification;augmented Lagrangian Method for Linear Programming Using Smooth Approximation;the Coherent Multi-representation Problem for Protein Structure Determination;the Effects of Shift Generation on Staff Rostering;consumers Financial Distress: Prediction and Prescription Using machine learning;optimizing Classroom Assignments to Reduce Non-essential Interactions Among University-Level Students During Pandemics;introducing a New Metric for Improving Trustworthiness in Real Time Object Detection;a Numerical Scheme for a Generalized Fractional Derivative with Variable Order;a Risk-Cost Analysis for an Increasingly Widespread Monitoring of Railway Lines;a Collaborative Multi-objective Approach for Clustering Task Based on Distance Measures and Clustering Validity Indices;iranian Architectural Styles Recognition Using Image processing and Deep learning;a Quasi-extreme Reduction for Interval Transportation Problems;assignment of Unexpected Tasks in Embedded System Design Process Using Genetic Programming;Improving Handwritten Cyrillic OCR by Font-Based Synthetic Text Generator;a Critical Node-Centric Approach to Enhancing Network Security.

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A Lite Fireworks Algorithm with Fractal Dimension Constraint for Feature Selection 6

A Lite Fireworks Algorithm with Fractal Dimension Constraint...

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IEEE 6th international conference on Industrial Cyber-Physical Systems (ICPS)

作者： Zeng, Min Mo, Haimiao Liang, Zhiming Wang, Hua Guangdong Univ Sci & Technol Sch Comp Sci Dongguan Guangdong Peoples R China Hefei Univ Technol Sch Management Hefei Peoples R China Guangzhou Med Univ Affiliated Hosp 5 Dept Pharm Guangzhou Guangdong Peoples R China

ISBN: (纸本)9798350311259

As the use of robotics becomes more widespread, the huge amount of vision data leads to a dramatic increase in data dimensionality. Although deep learning methods can effectively process these high-dimensional vision data. Due to the limitation of computational resources, some special scenarios still rely on traditional machine learning methods. However, these high-dimensional visual data lead to great challenges for traditional machine learning methods. therefore, we propose a Lite Fireworks Algorithm with Fractal Dimension constraint for feature selection (LFWA+FD) and use it to solve the feature selection problem driven by robot vision. the "LFWA+FD" focuses on searching the ideal feature subset by simplifying the fireworks algorithm and constraining the dimensionality of selected features by fractal dimensionality, which in turn reduces the approximate features and reduces the noise in the original data to improve the accuracy of the model. the comparative experimental results of two publicly available datasets from UCI show that the proposed method can effectively select a subset of features useful for model inference and remove a large amount of noise noise present in the original data to improve the performance.

关键词： Visual Data processing Feature Selection Fractal Dimension Fireworks Algorithm

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Decode Brain signal Into thai Word Using EEG and L-SVM 18

Decode Brain Signal Into Thai Word Using EEG and L-SVM

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18th international Joint Symposium on Artificial Intelligence and Natural Language processing (ISAI-NLP) / 4th international conference on Artificial Intelligence and Internet of things (AIoT)

作者： Adsawinnawanawa, Ekkarat Faragy, Md Soliman Buasod, Supakij Pruengkarn, Ratchakoon Keeratipranon, Narongdech Chulalongkorn Univ Dept Comp Engn Bangkok Thailand Dhurakij Pundit Univ Coll Innovat Technol & Engn Bangkok Thailand

ISBN: (纸本)9798350371215

this research decodes the brain signal into words with the help of machine learning and modern devices. It's been a long time since researchers tried to understand brain signals. Scientists succeed in decoding the brain signal using invasive methods such as ECoG. However, this process requires a dedicated skull surgery operation which poses a high risk to the subject. To ensure general-purpose availability, we propose a healthy non-invasive Brain-Computer Interface (BCI) system to convert brain signals to words. In this paper, we show the experiment result that proved EEG equipment has enough quality to decode brain signals to text with high accuracy. Our method uses EEG raw offline data, and processes with Statistic Features and Linear Support Vector machine (L-SVM) machine learning techniques from 2 thai native persons with 6 thai words including (sic) (Correct), (sic) (Help me), (sic) (Hungry), (sic) (No), (sic) (Stomachache), (sic) (thirsty) and we can achieve 100% accuracy.

关键词： BCI EEG ECoG L-SVM

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