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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

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Efficient Big Data Clustering Using Adhoc Fuzzy C Means and Auto-Encoder CNN 4th

Efficient Big Data Clustering Using Adhoc Fuzzy C Means and ...

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4th international conference on Inventive Computation and Information Technologies, ICICIT 2022

作者： Sreedhara, Shreehari Harthi Kumar, Vineet Salma, Subia Department of ECE S J C Institute of Technology Karnataka Chikkaballapur India Ramaiah Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9789811974014

Clustering, a well-known unsupervised machine learning technique is effective in handling massive amount of data for a variety of applications. Clustering is the process of grouping identical data into clusters that belong to a specific category. Big data consists of massive amounts of data generated every second;hence, clustering this data is quite challenging. Deep learning techniques are used to analyze large amounts of data, which necessitates the use of a large number of samples for training, resulting in a time-consuming and inefficient process. the adhoc fuzzy C means method can be used to overcome this limitation. In this research study, a fusion approach known as fusion clustering is built by combining auto-encoder features with adhoc fuzzy C means (AFCM) technique to improve the fusion approach. the auto-encoder, in combination with the CNN approach, is used for feature engineering to improve the performance, resulting in increased computational speed. the efficiency of the proposed model is improved by evaluating each performance metrics in detail by using three different data types: MNIST, Fashion-MNIST dataset, and USPS. the comparison of each dataset demonstrates that the proposed adhoc fuzzy C means (AFCM) model outperforms other state-of-the-art techniques. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Cooperative UAV Maneuver Decision-Making Based on Multi-Agent Reinforcement learning

Cooperative UAV Maneuver Decision-Making Based on Multi-Agen...

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Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE international conference on

作者： Weiyan Huo Jianxun Li School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

In this paper, we design a cooperative UAV maneuver decision-making task and use multi-agent reinforcement learning to solve it. In this task, two UAVs must learn cooperating with each other to defeat a stronger enemy. Due to sparse reward signal and insufficient exploration, the agents will learn a suboptimal policy. To mitigate this effect, the weighted entropy of reward is maximized to encouraged agent to explore while the expected cumulative reward is maximized. the weighted entropy of reward as an additional objective can be also optimized with reinforcement learning where the additional reward function is negatively correlated with the frequency of the reward obtained by agent. the test results show that compared with Multi-Agent Proximal Policy optimization (MAPPO), MAPPO with maximizing the weighted reward entropy has better performance.

关键词： Computer science Decision making Reinforcement learning Autonomous aerial vehicles Entropy optimization Intelligent control

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6th international conference on Communication, Devices and Networking, ICCDN 2022

6th International Conference on Communication, Devices and N...

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6th international conference on Communication, Devices and Networking, ICCDN 2022

ISBN: (纸本)9789819919826

the proceedings contain 60 papers. the special focus in this conference is on Communication, Devices and Networking. the topics include: Impact of Perceived Social Support on Patient Empowerment: A Study of Online Patient Support Groups;Interfacing of Industrial Inductive Proximity Sensor with NI DAQ and Speed Measurement;design and Implementation of Intelligent Vehicle Dual Vehicle Merging Based on Image Processing and Electromagnetic Induction;shield Tunnel Design Method Based on Constrained Multi-objective optimization Algorithm;railway Traffic Operation optimization Method and System Based on Bee Colony Algorithm;A Method and Display System for Interacting with Virtual Scene through VR Device;research on Dance Training System Based on Motion Capture and Virtual Reality;intelligent Motion Control Technology of Industrial Robot;Research on GIS Data Maintenance Technology Based on DBSCAN Algorithm;Design and Analysis of an Efficient Novel Multilayer 4:1 MUX Using QCA Technique with Reversibility and Stability;research on Matching Algorithm of Color;multi-band Dual-Polarized Linear-Circular and Linear-Cross Obliquely Stable Reflective Type Polarizer with Mechanically Tunable Reflector Response for K-and Ka-Bands applications;enrichment of Polarization Purity, Gain and Compactness of Circular Microstrip Antenna;Reconfigurable Dual Polarized Square Patch Antenna for ISM Band with PIN Diode Integration;design and Analysis of an On-Body Microstrip Patch Antenna for Biomedical Application;DHRNN: Deep Hybrid Recurrent Neural Networks for Aquaculture Water Quality Prediction;blockchain-Based Clean and Green Energy (Pico Hydel Power) Monitoring System;a Comparative Analysis to Predict Diabetes Using Classification algorithms;F-DDPT: An Efficient Fuzzy-Based Automated Preprocessing Technique to Support Explainability.

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3rd international Workshop on New Approaches for Multidimensional Signal Processing, NAMSP 2022

3rd International Workshop on New Approaches for Multidimens...

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3rd international Workshop on New Approaches for Multidimensional Signal Processing, NAMSP 2022

ISBN: (纸本)9789811978418

the proceedings contain 23 papers. the special focus in this conference is on New Approaches for Multidimensional Signal Processing. the topics include: Development of IoT Indoor Monitoring System for Independent Elderly;improving the Process of Evaluating User Stories Using the Paraconsistent Annotated Evidential Logic Eτ;neural Network Algorithm Applied in Electrical Engineering Automation;Truss Structure optimization Design Based on FE-PSO-SQP Algorithm;practice System of Ant Colony optimization Algorithm in Business Administration;literature Review of Smart Contracts Using Blockchain Technology;a Comprehensive Study of 5th Generation Scheduling algorithms;a Comparative Analysis of Homogeneous and Heterogeneous Protocols to Maximize the Lifetime of Wireless Sensor Network for Precision Agriculture;the Integration Development and Upgrading Path of Industry 4.0 Architecture Industrial Engineering Network Driven by Big Data;video Tracing of Moving Objects by Fusing three-Term Decompositions;SAAS Application Prospect Analysis in Hrm and Methods to Upgrade the Contemporary System;All Digital Phase Locked Loop (ADPLL) and Its Blocks—A Comprehensive Knowledge;on Realization of Smart Logistic Warehouse Management with Internet of things;development and Design of Intelligent Traction System for Bulk Cargo Terminal;deep learning Approaches for Classroom Audio Classification Using Mel Spectrograms;Tensor Spectral Pyramid for Color Video Sequences Representation, Based on 3D FO-AHKLT;electromyography Signal Acquisition, Processing, optimization and Its applications;research on the Radar Signal Classification Method Based on the Deep Faith Network Model;measuring Machine Intelligence Using Black-Box-Based Universal Intelligence Metrics;COVID Detection Using ECG Image Reports: A Survey;disease Detection Techniques in Plants: Transition from Manual to Automation.

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Machine learning Model for Medical Data Classification for Accurate Brain Tumor Cell Detection 4th

Machine Learning Model for Medical Data Classification for A...

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4th international conference on Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2022

作者： Navabothu, Gnana Sri Sai Sujith Sakode, Himanshu Gottipati, Jagathi Rama Devi, Polagani Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada520007 India

ISBN: (纸本)9789819916641

the health industry has grown into a large organization, and a vast number of medical data can be produced every day by the healthcare industry to collect information for the prediction of future diseases that can be produced by a patient using treatment history and health information. In a variety of medical diagnostic applications, automated defect identification in medical imaging has become an arising sector. Automated brain tumor identification and categorization are the focus of our work. In most cases, CT or MRI scans are used to examine the anatomy of the brain. the main objective of brain tumor detection is to help in clinical diagnosis. the goal is to design a machine learning method that ensures the existence of a tumor and to provide a reliable technique of tumor diagnosis that aids medical staff to provide proper medication to the patient. We get deep convolutional neural network (CNN) models that have already been trained using the transfer learning method to extract features from brain MRI scans. the Visual Geometry Group (VGG 16) is the most notable model when it comes to image recognition architectures as it surpasses and outperforms other traditional convolutional neural network models. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Application for the Recognition of Peruvian Sign Language using a Recurrent Neural Network

Application for the Recognition of Peruvian Sign Language us...

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Advanced learning Technologies on Education & Research (ICALTER), IEEE international conference on

作者： José Chacón-Córdova Jorge Tafur-Pastor Mónica Priale Alvaro Aures-García Software Engineering Faculty Peruvian University of Applied Sciences

ISBN: (数字)9798331510244

ISBN: (纸本)9798331510251

Research into effective communication between individuals with hearing disabilities and listeners remains a significant challenge that requires innovative technological solutions. this study focuses on the development of a mobile application leveraging a Recurrent Neural Network (RNN), specifically a Long Short-Term Memory (LSTM) model, to facilitate smooth communication of the Peruvian sign language. Initial experiments yielded promising results, with the system achieving 98% accuracy and a 3-second response time. Tests were conducted with 40 users, divided between those proficient in sign language and those unfamiliar with it. However, due to the lack of an existing Peruvian Sign Language dataset, a new dataset was created from scratch, consisting of 15 distinct gestures. Future iterations will need to address this limitation to optimize performance with a larger dataset.

关键词： Training Sign language Accuracy Recurrent neural networks Translation Assistive technologies Mobile applications Time factors Long short term memory optimization

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Improved MPPT Based on Flying Squirrel Search optimization Using SEPIC for PV Systems

Improved MPPT Based on Flying Squirrel Search Optimization U...

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Power Engineering Application (ICPEA), IEEE international conference in

作者： Indra Setyawan Rizki Mendung Ariefianto Rini Nur Hasanah Hadi Suyono Department of Electrical Engineering Universitas Brawijaya Malang Indonesia

the electricity production from PV systems depends on local solar radiation levels, as well as other prevailing meteorological characteristics such as temperature, dust, and humidity. Consequently, the electricity parameters such as voltage, current, and power levels will vary. To address this issue, PV systems use maximum power point tracking (MPPT) to regulate the voltage and current for producing the maximum power. the main goal is to regulate the PV system to achieve the maximum power or the power value as close to maximum as possible, even under varying environmental conditions. A metaheuristic approach such as the flying squirrel search optimization (FSSO) provides an advanced method for MPPT strategies. Apart from the algorithm method, this can be improved by using the suitable DC–DC converter. A SEPIC converter can be chosen as the medium for FSSO. Hence, this study presents the evaluation of the FSSO’s application in the SEPIC converter for PV systems. the simulation results show that FSSO on a SEPIC converter produces minimal ripple on both voltage and current, particularly when compared to FSSO on a common Boost converter. the FSSO-on-SEPIC converter configuration could reach a steady state at 0.4 s. Moreover, the configuration was also able to produce the MPPT’s efficiency of around 99.93%, which is slightly higher than that of the FSSO-on-Boost converter.

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A Gait Control Method for Bipedal Robots Based on Reward Function optimization

A Gait Control Method for Bipedal Robots Based on Reward Fun...

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Intelligent Communications and Computing (ICC), international conference on

作者： Ruoyan Pei Pengge Ma Tong Zhang Lan Li Zhengzhou University of Aeronautics Zhengzhou China Beijing Institute of Technology Beijing China

ISBN: (数字)9798331530365

ISBN: (纸本)9798331530372

the intelligent control of walking of humanoid robots is one of the key research directions in the field of robotics research, but the traditional motion model's constraint on the center of mass makes it difficult for robots to maintain walking stability and simulate human gait at the same time for its excessive attention on stability. Compared with the traditional deep reinforcement learning algorithm, the DDPG algorithm has the ability to handle continuous action space and high-dimensional state space, and has a wide application prospect in many practical problems. We proposed a bipedal robot control method based on reward function optimization to enable the robot to achieve both stable high-speed movement and human gait imitation. this paper combines the physical characteristics of the bipedal robot to establish a control system based on the DDPG algorithm. At the same time, the reward function is designed to guide the robot to learn the correct walking strategy. through the comparative test, the weight limit ratio of each reward function for the bipedal robot to stably increase the speed is given. the simulation results show that the method proposed in this paper has good practicality and effectiveness.

关键词： Legged locomotion Analytical models Robot kinematics Heuristic algorithms Robot control Aerospace electronics Stability analysis Robots optimization Intelligent control

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Wireless String: Machine learning-Based Estimation of Distance Between Two Bluetooth Devices 4th

Wireless String: Machine Learning-Based Estimation of Distan...

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4th international conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Saha, Mritunjay Talyang, Hibu Marchang, Ningrinla North Eastern Regional Institute of Science and Technology Arunachal Pradesh Nirjuli Itanagar791109 India

ISBN: (纸本)9789819900466

Determining the distance between two wireless devices, which is seemingly a trivial task, is the foundation upon which various applications can be built. An example of current interest due to the COVID-19 pandemic is a social distancing application that sets off an alarm when a device enters the vicinity of another device. However, accuracy in distance estimation between two devices is paramount for the viability of these applications. With this in mind, we present a study on the estimation of the distance between two Bluetooth-enabled devices based on machine learning, viz., regression. First, we propose a scheme for exchanging location coordinates between two devices through Bluetooth. Using this scheme, we generate a dataset of location coordinates between two devices and the actual distance between them. We then use this dataset to predict the distance between two devices given the location coordinates. We also use another existing dataset of Received Signal Strength Indicator (RSSI) measurements between two Bluetooth-enabled devices for prediction and compare the regression results. From the results, we confirm the viability of using a regression for solving the said problem and also the effectiveness of using location coordinates instead of RSSI. In particular, we find that non-linear regressors such as Decision Tree and Random Forest successfully estimate the distance with very good precision (with normalized mean absolute error within 0.03), whereas linear ones do not. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Comparative Analysis of RL-Based Resource Allocation Methods for optimization in 5G MMWave Network 5th

Comparative Analysis of RL-Based Resource Allocation Methods...

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5th EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Shilpa, V. Ranjan, Rajeev School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Applications REVA University Bangalore India

ISBN: (纸本)9783031770746

In this study, resource allocation techniques based on reinforcement learning (RL) for 5G millimeter wave (mmWave) networks are compared and analyzed. the high bandwidth and large available spectrum in mmWave networks offer a great opportunity for high data rate communication but also pose significant challenges in terms of resource allocation. RL, being a powerful tool for decision making in dynamic and uncertain environments, has been widely studied for resource allocation in mmWave networks. In this paper, we survey the state-of-the-art RL-based resource allocation methods for mmWave networks and we also discuss the challenges and open research directions in this field. this study presents an effective method for balancing stable, low-capacity mmWave links with reliable, high-capacity backhaul links designed using stochastic geometry and analytical channel modeling. We also propose a backhaul resource allocation strategy. To increase system utility with a mmWave network’s limited backhaul capacity, a neural network-based backhaul resource allocation has been proposed. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： 5G mobile communication systems

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