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7th international conference on Data Science and Engineering, ICDSE 2021

7th International Conference on Data Science and Engineering...

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7th international conference on Data Science and Engineering, ICDSE 2021

ISBN: (纸本)9789811944529

the proceedings contain 15 papers. the special focus in this conference is on Data Science and Engineering. the topics include: A CNN Approach for Detecting Red and White Lesions in Retinal Fundus Images;Predicting IMDB Movie Ratings Using RoBERTa Embeddings and Neural Networks;Domain-Specific Type-Safe APIs for Hierarchical Scientific Data with Modern C++;kernelized Transfer Joint Matching for Unsupervised Domain Adaptation;towards an Enhanced Understanding of Bias in Pre-trained Neural Language Models: A Survey with Special Emphasis on Affective Bias;exploring Rawlsian Fairness for K-Means Clustering;Hybrid Explainable Educational Recommender Using Self-attention and Knowledge-Based Systems for E-Learning in MOOC Platforms;an Improved Recommendation System with Aspect-Based Sentiment Analysis;Exploring Biomarker Identification and Mortality Prediction of COVID-19 Patients Using ML algorithms;COVID-19 Cases Prediction Based on LSTM and SIR Model Using Social Media;joint Geometrical and Statistical Alignment Using Triplet Loss for Deep Domain Adaptation;virtual Try-On Using Style Transfer.

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An Evolutionary Metagraph Approach for Solving Problems in Complex Subject Areas

An Evolutionary Metagraph Approach for Solving Problems in C...

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international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Anatoly N. Nardid Stepan S. Vinnikov Alexey V. Orazov Yuriy E. Gapanyuk Information Processing and Management Systems Department Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

this paper explores the integration of metagraphs with genetic programming (GP), offering a novel perspective aimed at overcoming the limitations of traditional graph-based approaches. Metagraphs, providing an advanced abstraction over standard graphs, are examined for their potential to more efficiently encapsulate complex relationships and hierarchical structures. the focus is on how metagraphs can contribute to evolutionary algorithms by enriching the representation of problem spaces, potentially leading to improved adaptability and precision in solutions. We discuss the initial theoretical insights and potential benefits of this integration, positioning metagraphs as a promising tool for enhancing the effectiveness of evolutionary strategies. this exploration is intended to pave new research pathways in GP, proposing that metagraphs hold the potential to significantly augment the outcomes of evolutionary processes.

关键词： Power engineering Directed acyclic graph Genetic programming Evolutionary computation Benchmark testing Reproducibility of results complexity theory Standards

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Comparative study of time complexity of visual odometry for autonomous drivng 7th

Comparative study of time complexity of visual odometry for ...

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7th international conference on Advances in Control and Optimization of Dynamical Systems (ACODS)

作者： Jain, Ankur Roy, Binoy Krishna Noble Grp Inst Dept Comp Engn Junagadh Gujarat India Natl Inst Technol Silchar Dept Elect Engn Silchar 788010 India

Autonomous navigation in real-life situations is a challenging task. Various sensors, like GPS, LiDAR, RADAR, etc. are used to localise an autonomous vehicle. there are some limitations in different approaches. A camera-based visual odometry is a promising technique. In this paper, the motion trajectory of an ego vehicle (having a single camera) is estimated in real-time using the CARLA simulator. A generalised flowchart for visual odometry is discussed. Five available algorithms for feature extraction and detection are compared based on their performances of the sequential images. the performances of two available algorithms for feature matching are also analysed. the comparison is done based on number of feature points detected, number of good matches, time complexity and feasibility of the algorithm. Six different variations of the sequential images from CARLA simulator, i.e. zoomed, panned, contrast, flipped, rotated, sheared images are considered to meet the real-life requirements. It is concluded that no set of available feature extraction, detection and matching algorithms is suitable to meet the requirements of all weather conditions. In comparison to other combinations, BRIEF-based Pipelines perform the best. Copyright (C) 2022 the Authors.

关键词： Visual odometry Time Compl exity Autonomous Driving

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A Scalable Solution for Pneumonia Diagnosis: Transfer Learning for Chest X-ray Analysis 7

A Scalable Solution for Pneumonia Diagnosis: Transfer Learni...

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7th international conference on Contemporary Computing and Informatics, IC3I 2024

作者： Haque, Rezaul Sultana, Shayma Prasad, Chandan Hasan, Sayed Fardin, Hasib Sakib, Abdullah Al Malik, Neeru Kaushik, Keshav East West University Department of Cse Dhaka Bangladesh International American University Department of Management Information System United States Pimpri Chinchwad University Department of Computer Science and Engineering Maharashtra Pune India Westcliff University Department of Engineering Management United States Westcliff University Department of Information Technology United States Pimpri Chinchwad University School of Engineering & Technology Maharashtra Pune India Amity University Amity School of Engineering and Technology Punjab India

ISBN: (纸本)9798350350067

the high death rate and rapid spread of pneumonia make it a serious global health concern. the precision of traditional diagnostic techniques is often compromised, particularly in resource-limited settings. Existing studies have predominantly focused on either binary or multi-class classification, often relying on limited datasets, which affects the generalizability and robustness of models. Additionally, the interpretability and transparency of deep learning models remain challenging in clinical environments. Addressing these challenges is crucial for advancing diagnostic methodologies to combat pneumonia. this study explores the use of transfer learning to improve the reliability of pneumonia classification using chest X-ray images. An automated classification system was developed, utilizing a combination of image preprocessing techniques, feature extraction with GLRLM, and various machine learning and transfer learning algorithms. Two datasets were employed: the GWCMCx dataset with 5856 chest X-rays and another dataset comprising 9207 chest X-rays. the system was designed for both binary and multi-class classification scenarios. CapsuleNet emerged as the most effective model, achieving an accuracy of 99.02% in binary classification and 90.43% in multi-class classification, significantly outperforming previous studies. Additionally, a user-friendly web application was developed to facilitate the integration of the classification system into clinical workflows, accelerating clinical decision-making. this study demonstrates the potential of transfer learning to automate and improve pneumonia diagnosis, offering a scalable solution that can alleviate radiologists' workload and improve patient outcomes. © 2024 IEEE.

关键词： Transfer learning

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Ultra-Short-Term Wind Speed Prediction Based on ICEEMDAN and SSA-LSTM

Ultra-Short-Term Wind Speed Prediction Based on ICEEMDAN and...

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China international Electrical and Energy conference (CIEEC)

作者： Yupeng Wang Zhenbin Zhang Haitao Li Zhen Li Yilin Wang School of Electrical Engineering Shandong University Jinan China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

Accurate ultra-short-term wind speed prediction plays a pivotal role in the operational efficiency, reliability and safety of wind power systems. But the complexity of wind speed makes it difficult to predict. In order to accurately predict the wind speed, an ultra-short-term wind speed prediction model is proposed. First, the original wind speed data are decomposed into a series of modal components from high frequency to low frequency using improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), which effectively reduces the complexity of the wind speed sequence. Second, for the large number of hyperparameters in the long short-term memory (LSTM), the sparrow search algorithm (SSA) is used to find the optimum. then LSTM is used to make the prediction. Next, the predicted sequences are superimposed and reconstructed to get the predicted data of wind speed. Finally, the improved model is compared with a single LSTM model as well as the ICEEMDAN-LSTM model. the comparison results show that the improved model predicts significantly better than the other models, and has better results for ultra-short-term wind speed prediction.

关键词： Adaptation models Empirical mode decomposition Wind speed Predictive models Wind power generation Prediction algorithms complexity theory

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Multi-Agent Deep Reinforcement Learning based Path Planning Algorithm for Multiple Automated Guided Vehicles in Large-scale Outdoor Scenarios

Multi-Agent Deep Reinforcement Learning based Path Planning ...

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7th international conference on Electronics Technology, ICET 2024

作者： Tengda, Wang Wenjun, Wu Yang, Sun Teng, Sun Beijing University of Technology Faculty of Information Technology Beijing China The 54th Research Institute of Cetc Shijiazhuang China

ISBN: (纸本)9798350363951

With the rapid development of intelligent technology, Automated Guided Vehicles (AGVs) are widely used in large outdoor scenes with complex terrain to serve various industrial activities. Since the collaboration of numerous AGVs has become a major trend, efficient Multi-Agent Path Finding (MAPF) algorithms are urgently needed. In this paper, the scenario served by multiple groups of AGVs is considered, in which each group of AGVs has the same destination. the path planning problem of each AGV is modeled as a Markov Decision Processes (MDP). the state is constructed based on the local environment information of all the grids around the AGV, including the terrain, occupation status and estimated cost of reaching the destination. the optimization objective is defined as the cumulative reward of the whole path containing the cost of passing through different terrains, the penalties for staying, the penalties for collisions and conflicts, and the rewards for reaching the destination. As all the AGVs plan their paths independently, the MAPF problem is a decentralized MDP. therefore, a distributed collaborative path planning algorithm based on Multi-Agent Advantage Actor-Critic (MA-A2C) is designed. Each AGV has a path planning agent, and the AGVs in the same group can share the parameters of the agent. Numerical simulation results validate the convergence of the proposed algorithm. Compared to the Waiting-stop A∗ algorithm, the proposed algorithm demonstrates better performance with regard to the cumulative reward, the ratio of arrival at destination, and the time of passage. © 2024 IEEE.

关键词： Automatic guided vehicles

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Modification of K-Means Clustering Algorithm for Optimizing School Zoning System Using Big Data

Modification of K-Means Clustering Algorithm for Optimizing ...

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international conference on Information and Communications Technology (ICOIACT)

作者： Eva Ayu Lestari Wahid Zahir Zainuddin Amil Ahmad Ilham Departement of Informatics Hasanuddin University Makassar Indonesia

ISBN: (数字)9798331536206

ISBN: (纸本)9798331536213

the increasing complexity of school zoning systems due to population growth and uneven geographic distribution has created significant challenges in ensuring balanced and fair allocation of students. this research focuses on modifying the K-Means clustering algorithm to optimize school zoning using Big Data. the admission of new students through the zoning system in Indonesia is crucial for enhancing access to quality education without discrimination. However, the current grouping process does not consider the distance between students' homes and schools. Clustering algorithms offer a solution by organizing data while accounting for the proximity between cluster members and their centroids. K-means clustering has shown promising results in grouping similar data but has limitations in determining centroid values, which can lead to suboptimal clustering. the modified K-Means algorithm is designed to address the limitations of traditional clustering methods in handling unbalanced group sizes, ensuring that each school zone has a proportional number of students. this study develops a clustering algorithm with fixed centroids to balance the number of members in each cluster. these algorithms are evaluated by calculating the total distance of each cluster member to its centroid. the proposed algorithms effectively cluster data and balance cluster membership, making them suitable for student placement in the zoning system. this research aims to provide a more effective solution for school zoning that can be applied to real-world educational systems, ultimately improving the fairness and efficiency of student distribution across schools.

关键词： Clustering methods Education Clustering algorithms Big Data Information and communication technology complexity theory Resource management

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UAMP-SBL Based Channel Estimation for Underwater Acoustic Communications with Receiver Array

UAMP-SBL Based Channel Estimation for Underwater Acoustic Co...

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IEEE international conference on Information Communication and Signal Processing (ICICSP)

作者： Huaji Ma Mingyuan Zhang Jun Tao Zhengbo Wang Key Laboratory of Underwater Acoustic Signal Processing of the Ministry of Education School of Information Science and Engineering Southeast University Nanjing China School of Biological Science and Medical Engineering Southeast University Nanjing China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Compared with wireless channels, underwater acoustic (UWA) channels are much more challenging. To achieve satisfactory performance, a receiver array is commonly employed to achieve diversity gain or array gain. In UWA communications, people has focused on diversity gain of the receiver array and the array gain is generally overlooked. With the advent of massive MIMO technique, some recent efforts have been made to make use of array gain in UWA communications. In this case, angle information needs to be explicitly incorporated into the UWA channel model. In this paper, we propose a channel estimation scheme based on the combination of UAMP and SBL algorithms, achieving a tradeoff of performance and complexity. In the proposed scheme, angle, delay, and amplitude of the UWA channel are all estimated. Simulation and experimental results both verified the UAMP-SBL channel estimation scheme outperforms traditional channel estimation methods.

关键词： Wireless communication Channel estimation Receivers Diversity methods Acoustic arrays Delays complexity theory Bayes methods Underwater acoustics Singular value decomposition

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Task-Oriented Service Provision and Scheduling for Non-Terrestrial Networks: A Deep Reinforcement Learning Based Approach 16

Task-Oriented Service Provision and Scheduling for Non-Terre...

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16th international conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Cao, Yilu Jia, Ziye Zhang, Yu Dong, Chao Zhou, Fuhui Wu, Qihui Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Jiangsu Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Jiangsu Nanjing211111 China School of Engineering Newcastle University Newcastle upon TyneNE1 7RU United Kingdom

ISBN: (纸本)9798350390643

the non-terrestrial network (NTN) receives considerable attentions due to the development of the sixth generation technologies in recent years. However, the mobility and heterogeneity of satellites and unmanned aerial vehicles in NTN lead to increasing challenges in resource allocation and management. As such, the network function virtualization technology is introduced, by deploying service function chains (SFCs) to achieve requirements. However, it brings conflicts and resource competition due to urgent unexpected tasks. therefore, scheduling multiple SFCs in NTN is a crucial issue. In this paper, a task-oriented service provision and scheduling model is proposed to tackle the dynamic complexity of NTN by designing the reconfigurable time extension graph. Subsequently, the SFC scheduling problem is formulated to maximize the number of completed tasks within limited resources and time constraints. Due to the problem form of integer linear programming, we propose a deep reinforcement learning based algorithm. Finally, simulation results validate the effectiveness of the proposed algorithm, indicating its superior performance over other benchmark algorithms. © 2024 IEEE.

关键词： Deep reinforcement learning

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7th international conference on Business Intelligence, CBI 2022

7th International Conference on Business Intelligence, CBI 2...

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7th international conference on Business Intelligence, CBI 2022

ISBN: (纸本)9783031064579

the proceedings contain 23 papers. the special focus in this conference is on Business Intelligence. the topics include: Intelligent System Based on GAN Model for Decision Support in Brain Tumor Segmentation;a Convolutional Neural Networks-Based Approach for Potato Disease Classification;a Novel Hybrid Approach for Improving the Accuracy of the Supervised Link Prediction Based on Graph Structure Features in Social Networks;Analysis of Several algorithms for DOA Estimation in Two Different Communication Models by a Comparative Study;how far can Deep Learning Improve Arabic Part of Speech Tagging?;analysis of Decision Tree algorithms for Diabetes Prediction;automatic Change Detection Based on the Independent Component Analysis and Fuzzy C-Means Methods;automatic Text Summarization for Moroccan Arabic Dialect Using an Artificial Intelligence Approach;mining Frequents Itemset and Association Rules in Diabetic Dataset;Increasing Student Engagement in Lessons and Assessing MOOC Participants through Artificial Intelligence;comparative Study of Deep Learning Models for Detection and Classification of Intracranial Hemorrhage;optimization Focused on Parallel Fuzzy Deep Belief Neural Network for Opinion Mining;deep Learning Model for Educational Recommender Systems;sentiment Analysis Decision System for Tracking Climate Change Opinion in Twitter;an Exploration of Student Grade Retention Prediction Using Machine Learning algorithms;deep Reinforcement Learning for Bitcoin Trading;machine Learning Approach to Automate Decision Support on Information System Attacks;Facial Expression Recognition Using a Hybrid ViT-CNN Aggregator;decision Boundary to Improve the Sensitivity of Deep Neural Networks Models;Performance Investigation of a Proposed CBIR Search Engine Using Deep Convolutional Neural Networks;preface.

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