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4th Congress on intelligent Systems, CIS 2023

4th Congress on Intelligent Systems, CIS 2023

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4th Congress on intelligent Systems, CIS 2023

ISBN: (纸本)9789819990368

the proceedings contain 35 papers. the special focus in this conference is on intelligent Systems. the topics include: Track learning Agent Using Multi-objective Reinforcement learning;performance Assessment of Gaussian Filter-Based Image Fusion Algorithm;a Cognitive Comparative Analysis of Geometric Shape-Based Cryptosystem;a Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer;energy optimization of Electronic Vehicle Using Blockchain Method;pattern Recognition: An Outline of Literature Review that Taps into Machine learning to Achieve Sustainable Development Goals;novel Approach for Stock Prediction Using Technical Analysis and Sentiment Analysis;visualizing and Exploring the Dynamics of optimization via Circular Swap Mutations in Constraint-Based Problem Spaces;An Approach to Increase the Lifetime of Traditional LEACH Protocol Using CHME-LEACH and CHP-LEACH;denseMammoNet: An Approach for Breast Cancer Classification in Mammograms;aspect-Based Sentiment Classification Using Supervised Classifiers and Polarity Prediction Using Sentiment Analyzer for Mobile Phone Tweets;microbial Metabolites and Recent Advancement;design of a 3D-Printed Accessible and Affordable Robotic Arm and a User-Friendly Graphical User Interface;profit Maximization of a Wind-Integrated System by V2G Method;detection of Partially Occluded Area in Images Using Image Segmentation Technique;Application of IP Network Modeling Platforms for Cyber-Attack Research;enhancing Information Integrity: Machine learning Methods for Fake News Detection;Optimum Selection of Virtual Machine in Cloud Using Improved ACO;data Imputation Using Artificial Neural Network for a Reservoir System;depth Multi-modal Integration of Image and Clinical Data Using Fusion of Decision Method for Enhanced Kidney Disease Prediction in Medical Cloud;an Efficient Prediction of Obstructive Sleep Apnea Using Hybrid Convolutional Neural Network.

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4th Congress on intelligent Systems, CIS 2023

4th Congress on Intelligent Systems, CIS 2023

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4th Congress on intelligent Systems, CIS 2023

ISBN: (纸本)9789819990399

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Real-time Rice Disease Spot Segmentation Using an Improved YOLOv8 Framework 4

Real-time Rice Disease Spot Segmentation Using an Improved Y...

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4th International conference on Machine learning and intelligent Systems Engineering (MLISE)

作者： Yang, Haichao Jiang, Yue Xiao, Weijun Huang, Xumin Dali Univ Coll Math & Comp Sci Dali Peoples R China

ISBN: (纸本)9798350375084;9798350375077

In modern agriculture, early and accurate quantitative management of crop diseases is key to improving yield and quality, but traditional methods are inefficient and not accurate enough. To address this challenge, this paper focuses on the use of instance segmentation, to improve the real-time detection and quantification of rice leaf diseases and to solve the segmentation challenges in complex backgrounds. the feature extraction and fusion part of YOLOv8 is optimised by introducing the Large Separable Kernel Attention (LSKA) mechanism and the Bidirectional Feature Pyramid Network (BiFPN) to improve the model's ability to detect and segment lesions in complex environments. the implementation results show that the improved YOLOv8 achieves an average accuracy of 80.6% at IoU=0.5, which improves the lesion segmentation accuracy while maintaining an efficient processing speed, and the segmentation effect significantly reduces the background interference and improves the lesion localisation accuracy. this study successfully improves the real-time segmentation performance of rice leaf spots, especially the high accuracy and robustness under complex background, which provides an advanced automated detection tool for plant disease management.

关键词： crop disease instance segmentation rice rust spot yolo deep learning

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A Coordinated Active and Reactive Power Dynamic optimization Method for Distribution Networks Based on Linearized Voltage Sensitivity 4

A Coordinated Active and Reactive Power Dynamic Optimization...

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4th International conference on intelligent Power and Systems, ICIPS 2024

作者： Ge, Xiaoning Nie, Yang Shi, Yuxin Yang, Yang State Nuclear Electric Power Planning Design & Research Institute Co. Ltd Beijing China Wuhan University School of Electrical Engineering and Automation Wuhan China

ISBN: (纸本)9798331532765

Rapid growth in renewable energy installed capacity poses new challenges to the traditional distribution network control. the high penetration of distributed photovoltaic (PV) systems introduces issues such as voltage violations and reverse power flow, which are difficult to manage using conventional control methods. Against this backdrop, feeder load has emerged as a promising resource for active power regulation due to its aggregative properties. this paper proposes a coordinated active and reactive power optimization method based on voltage sensitivity, incorporating feeder load power control into the distribution network optimization framework. By leveraging the ZBus model and a fixed-point iteration method, the approach achieves sensitivity linearization and rapid power flow calculation. the proposed method enhances control efficiency and renewable energy utilization, while ensuring the safety, power quality, and economic operation of the distribution network. Furthermore, an active-reactive power optimization model is developed and solved, providing technical support for the optimization of modern power systems. © 2024 IEEE.

关键词： Load flow optimization

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A Comparative Study of Deep learning Models for Brain Tumor Classification 4

A Comparative Study of Deep Learning Models for Brain Tumor ...

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4th International conference on intelligent Technologies, CONIT 2024

作者： Das, Tanmoy Vishwakarma, Dinesh Kumar Ranga, Virender Delhi Technological University Department of Information Technology New Delhi India

ISBN: (纸本)9798350349900

Brain tumor is a fetal disease nowadays that leads to cancer, so detecting brain tumors at an early stage is very crucial. there are many deep neural networks available in the field of medical image processing to detect brain tumors. this paper compares four deep-learning techniques for classifying brain tumor. the models that are used in this model are DenseNet-121, ResNet-50, VGG-16 and Inception-V3. the performance evaluation is based on accuracy, precision, F1-score, AUC ROC score, and Cohen Kappa score on the brain-tumor-detection-mri Dataset from Kaggle. A total of 2400 + images belonging to two classes. We have an accuracy of 9 4. 5% for DenseNet-121, 97.5% for ResNet-50, and 94% for VGG-16 and Inception-V3, and we have a score of 73%. the results provide insights into the strengths and weaknesses of each technique and offer guidance for selecting the most suitable approach for image classification in the medical imaging domain. © 2024 IEEE.

关键词： Contrastive learning

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Improved U-Net with gray channel attention for image segmentation 4

Improved U-Net with gray channel attention for image segment...

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4th International conference on intelligent Communications and Computing

作者： Pan, Feng Geng, Lujing Zhang, Ning Chen, Zuhao China Mobile Grp Design Inst Co Ltd Network Super Prod Dept Beijing Peoples R China China Mobile Grp Co Ltd Network Business Dept Beijing Peoples R China

ISBN: (纸本)9798331530372;9798331530365

Image segmentation base on deep learning methods is an important direction in computer vision field. However, these models over-rely on color features in image segmentation tasks, which leads to poor segmentation effect in scenes with the interference of similar background colors. To solve this problem, this paper successfully improves the U-Net model by introducing the technical means of combining gray channel and attention mechanism. the experimental results show that compared with the original U-Net model, the average accuracy of the improved U-Net with gray channel attention has increased from 81.69% to 82.61%. At the same time, we apply this method mechanism to improved models of U-Net such as Attention U-Net and R2U-net, and similar effect is verified. these results verify that the combination of gray channel and attention mechanism can effectively improve the robustness and accuracy of deep learning model when processing color-similar background in image segmentation tasks. this work has important practical application value and provides a new solution for image segmentation tasks in complex scenes.

关键词： image segment U-Net attention mechanism deep learning

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Payload Parameters Identification Using Incremental Ensemble learning 4

Payload Parameters Identification Using Incremental Ensemble...

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4th International conference on Computer, Control and Robotics (ICCCR)

作者： Taie, Wael ElGeneidy, Khaled Al-Yacoub, Ali Ronglei, Sun Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan Peoples R China October Univ Modern Sci & Arts Mech Syst Engn Dept Cairo Egypt Coventry Univ Sch Engn Cairo Egypt Loughborough Univ EPSRC Ctr Innovat Mfg Intelligent Automat Loughborough Leics England Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan Peoples R China

ISBN: (纸本)9798350373141;9798350373158

this paper introduces a new method for identifying the payload inertial parameters for collaborative robots (cobots) that operate in dynamic manufacturing environments with frequent reconfiguration. the proposed method eliminates the need for specific excitation trajectories during the identification process, allowing the identification of payload parameters while the cobot follows any arbitrary task trajectory. this is achieved via an incremental ensemble model, which utilizes incremental neural networks as weak learners. the proposed method successfully adapts the ensemble model to new task paths while maintaining accurate estimations for the payload parameters. the mean absolute error for mass, center of mass, and the moment of inertia parameters were 0.01 kg, 0.0068 kg.m, and 0.0008 kg.m(2), respectively. these results demonstrate the effectiveness of the model in dealing with new task trajectories in comparison to traditional or batch ensemble models.

关键词： payload dynamics incremental learning ensemble learning parameters identification

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Adaptive Investment Strategies: Deep Reinforcement learning Approaches for Portfolio optimization 4

Adaptive Investment Strategies: Deep Reinforcement Learning ...

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4th International conference on intelligent Technologies, CONIT 2024

作者： Sharma, Rishabh Sharma, Ajay Hariharan, Shanmugasundaram Jain, Vishal Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India

ISBN: (纸本)9798350349900

this paper addresses the role deep reinforcement learning (DRL) plays in portfolio management by making a timely and more accurate market prediction within the finance sector. Using these machines learning the methods, including the DRL algorithm, this program is designed to strengthen while making investment choices beyond the limits of the traditional framework, as well as enhancing the decision-making processes in an environment with many uncertainties and dynamics. the study begins with a literature review that is thorough and covers past works on traditional finance portfolio management, market prediction methods, and recent work on DRL in finance. Leveraging this base, we design and implement DRL-V-based models for asset management and market prediction situations. Empirical research includes the construction of models that use historical financial records about stock indexes and macroeconomic data as the main input. We use some of the performance metrics like cumulative returns, Sharpe ratio, maximum drawdown, and annualized volatility as the KPIs. We compare them with historical data along with the DRL model's performance and other baseline models as well. the evidence obtained from the experiments proves that DRL-based investment strategies outperform other strategies by their ability to make adjustments for changing market standards and uncovering investment opportunities. Besides the comparative studies, the variety of DRL models used in DFS which perform similar tasks under different market environments has been validated. © 2024 IEEE.

关键词： Deep reinforcement learning

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Research on intelligent Refined Management of Military Electronic Component Inventory 4

Research on Intelligent Refined Management of Military Elect...

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4th International conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Wang, Dong Jiangsu Automation Research Institute Jiangsu Lianyungang China

ISBN: (纸本)9798331534622

As modern military equipment becomes increasingly complex and diversified, the role of military electronic components in military systems has grown significantly. Traditional inventory management faces inefficiencies and inaccurate data issues, while intelligent inventory management technologies have significantly improved management efficiency and precision [1] [2]. this paper, based on the characteristics of military electronic components, explores the current state, key technologies, and implementation paths of intelligent inventory management. It focuses on improved measures for storage route optimization and material classification management, proposing a refined management model based on the Internet of things (IoT) [3] [4], big data analytics [5] [6], and cloud computing [7] [8]. the research provides theoretical support and practical guidance for precise management of military electronic components and ensures operational readiness. © 2024 IEEE.

关键词： Information management

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Research on digital advertising precision delivery system based on reinforcement learning algorithm 4

Research on digital advertising precision delivery system ba...

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4th International Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Zhao, Te Nottingham Trent University Nottingham United Kingdom

ISBN: (纸本)9798331528676

this study proposes a new reinforcement learning framework for Trans-Proximal Policy (TPP), which aims to combine the advantages of Transformer network and Proximal Policy optimization (PPO) to realize efficient and precise digital advertising. TPP uses the powerful sequence processing capability of Transformer network to capture historical patterns of user behavior, and combines the efficient policy optimization algorithm of PPO to achieve fast and stable policy learning. In the digital advertising scenario, TPP can capture the user's historical behavior sequence data to predict the future interaction probability, and optimize the advertising strategy accordingly. Finally, through comparative experiments and analysis between the model framework and traditional optimization algorithms, this paper verifies that the TPP proposed in this paper can significantly improve the click-through rate, accuracy rate and conversion rate of advertisements while maintaining the stability of policy update, especially in the processing of large-scale user behavior data. this model is proved to be feasible and effective in the digital advertising precision delivery system. ©2024 IEEE.

关键词： Marketing

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