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15th International Conference on Advanced Computer Theory and engineering, ICACTE 2022

作者： Mo, Jiehong Yu, Yongbin Zhou, Chen Zhong, Yuanjingyang Wang, Zipeng University Of Electronic Science And Technology Of China School Of Information And Software Engineering Chengdu China

ISBN: (数字)9781665481809

ISBN: (纸本)9781665481809

In this paper, a new variant of genetic algorithm, matrix-based genetic algorithm (MGA), is proposed, which represents the population of genetic algorithm by matrix, and achieves evolution by matrix operation. Applying it to the 2D global path planning problem of robot, MGA has better accuracy and running speed than the basic genetic algorithm. Specifically, the average path length obtained after convergence of MGA is closer to the actual shortest path length than that of the basic genetic algorithm, and, due to the parallelism of the MGA in matrix operations, the MGA runs in 1/2 the time of the traditional genetic algorithm when using the NumPy library. Two experiments comparing shortest path lengths demonstrate that MGA has better stability and can cope with complex environments. In order to enable the algorithm to have better accuracy, this paper also explores the effect of whether to use elite strategy on the accuracy of the shortest path of the algorithm. In addition, the effect of different pathfinding algorithms on the computing time of the algorithm is explored to speed up the operation of the algorithm. © 2022 IEEE.

关键词： Genetic algorithms

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An Empirical Comparison of Machine and Deep Learning Algorithms for Predicting Maternal Health Risk 5th

An Empirical Comparison of Machine and Deep Learning Algorit...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： N, Srinivasa Gupta Valarmathi, B. Kavitha, A. Gopini, Sai Bhuvan Seela, Hari Kumar Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced of Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9783031770777

Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, high rates of maternal as well as infant morbidity and mortalities are recorded. This research utilizes Artificial Intelligence (AI) with machine learning algorithms to forecast and address maternal health hazards right at their onset stage. The current research utilizes the concept of AI along with many Machine Learning (ML) methods like the Ensemble Learning Model (ELM), Random Forest (RF), K-Nearest Neighbour (KNN), Decision-Tree (DT), XG-Boost (XGB), Cat Boost (CB), and Gradient Boosting (GB), along with Synthetic Minority Over-sampling Technique (SMOTE) algorithm used for dealing with the problem class imbalance within the data set. SMOTE algorithm is utilized for the dataset balancing process. The handling system involves refining data preprocessing with the help of feature engineering and robust data cleaning which makes sure that anomalies do not erode the reliability of the predictive model. The existing methods [1] used RF (90%), DT (87%), XGB (85%), CB (86%), and GB (81%) algorithms and were compared with the accuracies of the proposed models like Logistic Regression (LR), Ensemble Learning Bagging (ELB), Ensemble Learning Stacking (ELS), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The existing methods used only imbalance dataset. The accuracies of the proposed models with using SMOTE algorithm (balanced dataset) are LR (61.33%), KNN (81%), ELB (92.33%), ELS (90.66%) CNN (40.67%), RNN (59.67%), LSTM (54%), GRU (56%) respectively. Among these methods, ELB achieved 92.33% of accuracy with using SMOTE algorithm using imbalanced dataset. Whereas the accuracies of the proposed models without using SMOTE algorithm (imbalanced dataset) are LR (66.09%), KNN (68.47%)

关键词： Convolutional neural networks

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Study on Prediction Mechanism of High-speed Traffic Accidents Based on Space-Time Meteorological Grid 4

Study on Prediction Mechanism of High-speed Traffic Accident...

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4th International Conference on Intelligent information Processing, IIP 2022

作者： Zhang, Wei Lu, Jun Zhao, Min Liang, Lei Tang, Ran Peng, Jiakai Chengdu University of Information Technology School of Software Engineering Sichuan Chengdu610225 China

ISBN: (纸本)9781665492225

Based on the traffic accident data of the Yaxi section of the G5 Beijing-Kunming high-speed from 2018 to 2021 and the observation data of the national meteorological station corresponding to the accident occurrence point provided by the Sichuan Meteorological Office, this paper studies the prediction mechanism of the high-speed traffic accident by using the Kmeans cluster analysis, space-time grid coding and multiple logistic regression model in SPSS algorithm. In this paper, the model analysis and prediction mechanism of highway traffic meteorological disaster in western Sichuan Plateau region are studied by using data resources from meteorological and transportation departments. In this paper, a fine intelligent grid space-time model is used to improve the granularity of model analysis and prediction. Sections of meteorological elements, this article will research traffic accident data, regression analysis on the data collection using smart grid model of time and space, with meteorological elements as the emphasis, build the traffic accident of time, space, space and time on the grid data model, aiming at key points in traffic accident risk accident and the regression analysis between grid meteorological elements of time and space, Thus the traffic meteorological risk prediction model is established. © 2022 IEEE.

关键词： Disasters

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Traffic Anomaly Detection Using Optimized Faster R-CNN

Traffic Anomaly Detection Using Optimized Faster R-CNN

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2024 International Conference on Electronics, Electrical and information engineering, ICEEIE 2024

作者： Karim, Shahid Qadir, Akeel Han, Gao Li, Jing Ibrar, Muhammad Bibi, Irfana School of Information Engineering Xi'an Eurasia University 710065 China Software College Shenyang Normal University Shenyang China Department of Computer Science Faculty of Computing and Information Technology University of Punjab Punjab Lahore Pakistan

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

This paper introduces a new method for traffic anomaly identification within the Faster R-CNN framework by combining the DenseNet module with the region proposals (RPs) processing block. The suggested approach seeks to improve feature extraction and RP creation in aerial imagery or CCTV frames to get beyond the drawbacks of the current methods. Discriminative features are extracted through DenseNet, and the RPs block makes it easier to provide a wide range of accurate and varied area proposals. Contrast correction techniques are applied to overcome the difficulties presented by low-resolution and low-contrast images frequently seen in surveillance scenarios. Comparing experimental results to state-of-the-art techniques, one can observe notable resilience and detection accuracy increases. The suggested method works exceptionally well in various traffic scenarios and environmental circumstances, indicating its potential for practical implementation in traffic surveillance systems. Overall, the study contributes to advancing the efficacy and reliability of anomaly detection in urban traffic environments. © 2024 SPIE.

关键词： Aerial photography

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Agile Knowledge Management Framework

Agile Knowledge Management Framework

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Nuha El-Khalili Software Engineering Department Information Technology University of Petra Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The paper proposes a novel Agile Knowledge Management (KM) framework designed to enhance organizational knowledge sharing and utilization. Acknowledging the limitations of traditional KM approaches, which often overemphasize technology at the expense of human factors, processes and strategy, we promote a more flexible and adaptable framework. This framework integrates agile methodology to create a more responsive, adaptable, and efficient KM system. The framework consists of three key components: roles, processes, and artifacts. It defines three distinct roles: cross-functional teams responsible for executing the KM lifecycle; a steering committee to monitor, evaluate, and guide the process; and interested parties who define knowledge needs. The core KM process is iterative, with each iteration consisting of five key stages: discovery, creation, structuring and documentation, sharing, and utilization. The framework recognizes two artifacts: knowledge repository and knowledge strategy. The integrated use of Large Language Models (LLMs) is emphasized in the framework to generate new forms of knowledge.

关键词： Technological innovation Large language models Digital transformation Organizations Learning (artificial intelligence) Human factors Documentation Knowledge management Iterative methods Monitoring

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SIAT: Pedestrian trajectory prediction via social interaction-aware transformer

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Complex & Intelligent Systems 2025年第8期11卷 1-14页

作者： Wang, Chengdong Wang, Jianming Gao, Wenbo Guo, Lei School of Computer and Software Engineering Anhui Institute of Information Technology Wuhu China Yunnan Key Laboratory of Computer Technology Applications Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

Pedestrian trajectory prediction is crucial for mitigating collision risks in intelligent transportation and surveillance systems. Despite recent advances, accurately capturing and modeling complex social interactions among pedestrians remains a challenge. This paper introduces the Social Interaction-Aware Transformer (SIAT), a novel approach that leverages a Transformer encoder to process pedestrian embedding features and a Graph Convolutional Network (GCN) to construct a social graph for extracting spatial interaction features. The future pedestrian trajectory is predicted using a Transformer decoder that integrates both pedestrian embeddings and social graph features. Extensive experiments on the ETH/UCY and Stanford Drone datasets demonstrate that SIAT significantly outperforms state-of-the-art methods in terms of accuracy and robustness, particularly in densely populated environments. SIAT’s contributions include improved precision through temporal and spatial processing, deep contextual understanding of pedestrian dynamics, and robustness across various settings. The novel model framework establishes a new benchmark for mixed models in trajectory prediction.

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Aspect-based Sentiment Analysis with External Knowledge Embedding and Syntactic information 23

Aspect-based Sentiment Analysis with External Knowledge Embe...

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Proceedings of the 2023 7th International Conference on Electronic information technology and Computer engineering

作者： Fan Zhang Wenbin Zheng School of Software Engineering Chengdu University of Information Technology China

ISBN: (纸本)9798400708305

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis. The current research mainly focuses on context-based and syntactic dependency-based approaches. However, they only consider the inherent syntactic connections and linguistic properties within the sentence, and much additional knowledge is not considered or utilized. To handle this concern, we propose a knowledge embedding and syntactic information enhancement network (KESE). KESE aims to fully utilize syntactic dependency and introduce external knowledge for assisting in mining the connection between aspect and sentiment words. We utilize an aspect-oriented attention mechanism to capture aspect features and enhance them with syntactic masking networks and graph convolutional networks. Furthermore, we embed knowledge graphs to improve the recognition of aspects and sentiments. We conduct experiments on several publicly accessible ABSA datasets, and the results demonstrate the effectiveness of our proposed KESE.

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Missing Values for Classification of Machine Learning in Medical data 5

Missing Values for Classification of Machine Learning in Med...

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5th IEEE International Conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Ren, Lijuan Wang, Tao Seklouli, Aicha Sekhari Zhang, Haiqing Bouras, Abdelaziz Lyon France Chengdu University of Information Technology School of Software Engineering Chengdu China

ISBN: (纸本)9781665499132

Missing values are an unavoidable problem for classification tasks of machine learning in medical data. With the rapid development of the medical system, large scale medical data is increasing. Missing values increase the difficulty of mining hidden but useful information in these medical datasets. Deletion and imputation methods are the most popular methods for dealing with missing values. Existing studies ignored to compare and discuss the deletion and imputation methods of missing values under the row missing rate and the total missing rate. Meanwhile, they rarely used experiment data sets that are mixed-type and large scale. In this work, medical data sets of various sizes and mixed-type are used. At the same time, performance differences of deletion and imputation methods are compared under the MCAR (Missing Completely At Random) mechanism in the baseline task using LR (Linear Regression) and SVM (Support Vector Machine) classifiers for classification with the same row and total missing rates. Experimental results show that under the MCAR missing mechanism, the performance of two types of processing methods is related to the size of datasets and missing rates. As the increasing of missing rate, the performance of two types for processing missing values decreases, but the deletion method decreases faster, and the imputation methods based on machine learning have more stable and better classification performance on average. In addition, small data sets are easily affected by processing methods of missing values. © 2022 IEEE.

关键词： Classification (of information)

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Distributed Feature Reduction Signal Sorting Algorithm

Distributed Feature Reduction Signal Sorting Algorithm

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International Conference on Guidance, Navigation and Control, ICGNC 2022

作者： Wan, Liangtian Wang, Jinjing Sun, Lu Wang, Xianpeng Lu, Cunbo Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian116026 China School of Information and Communication Engineering Hainan University Haikou570228 China Information Science Academy China Electronics Technology Group Corporation Beijing100098 China

ISBN: (纸本)9789811966125

With the development of science and technology, modern war has changed from traditional mechanized war to electronic war. Electronic warfare has become the mainstream combat mode in modern war. At present, the electromagnetic environment is very chaotic and complex, the pulse density increases rapidly, and the pulse modulation mode is complex and changeable. In order to deal with the low efficiency of traditional methods in complex electromagnetic environment, a distributed feature reduction signal sorting method is proposed. Firstly, attribute entropy regularization is added to the objective function, and different weights are given to different features. Secondly, the feature reduction process is added to select which dimension of features to be discarded by setting the threshold. Each iteration of the algorithm will update the membership matrix, cluster center and feature weight matrix until the termination conditions is satisfied. Simulation results show that the algorithm can reduce the number of iterations and time, and improve the accuracy of radar signal sorting results by reducing the number of features. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reduction

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Adversarial Domain Adaptation for Non-Intrusive LOAD Monitoring 20

Adversarial Domain Adaptation for Non-Intrusive LOAD Monitor...

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20th International Computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2023

作者： Bongyi, Gao Bei, Hui School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

ISBN: (纸本)9798350318982

Non-Intrusive Load Monitoring (NILM), known as energy disaggregation, is a method for determining the usage of separated devices by analyzing the overall energy consumption of an entire household. Understanding individual appliance energy consumption is crucial for customers to effectively manage usage and plan demand response programs. Recent advancements in data-driven methods have demonstrated promising performance in NILM. However, these methods rely on massive labeled data and would be dramatically degraded in novel environments. This paper presents ADAED, an energy disaggregation algorithm based on adversarial domain adaptation to monitor the loads of an unlabeled target domain by learning shared representations from a related source domain. Through the application of a sequence-to-point architecture and shared representations, ADAED can achieve more accurate predictions. Extensive experiments on three open benchmarks verify that our method can achieve better results than existing methods. © 2023 IEEE.

关键词： Energy utilization

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