Pub Date : 2022-12-01DOI: 10.1109/ACMLC58173.2022.00021
Nimai Chand Das Adhikari, Bijon Guha, Arpana Alka, Utsav Das
Cancer is a group of diseases caused by abnormal cell growth, eventually leading to death. Cancer symptoms include chronic cough, breathing difficulties, weight loss, muscle stiffness, oedema, and bruises. Cancer detection increases with the stages, but unfortunately, the fatality also increases. In this research, we propose a pipeline coined as PiXelNet, which uses a classification system based on Convolutional Neural Networks (CNNs) that identifies three distinct kinds of lung cancer on histopathological images. The first step of the proposed network consists of a medical imaging analysis pipeline with models like ResNet, Efficient NetBO and MobileNet. We found that EfficientNet outperforms the other two models with a test accuracy of 99.33% and a loss of 0.0066. The second stage involves identifying the key areas from the original input test image with the feature extracted values. Using this strategy, the doctor or pathologist will immediately access all the crucial imaging heat maps and the network analysis report.
{"title":"PiXelNet: A DL-Based method for Diagnosing Lung Cancer using the Histopathological images","authors":"Nimai Chand Das Adhikari, Bijon Guha, Arpana Alka, Utsav Das","doi":"10.1109/ACMLC58173.2022.00021","DOIUrl":"https://doi.org/10.1109/ACMLC58173.2022.00021","url":null,"abstract":"Cancer is a group of diseases caused by abnormal cell growth, eventually leading to death. Cancer symptoms include chronic cough, breathing difficulties, weight loss, muscle stiffness, oedema, and bruises. Cancer detection increases with the stages, but unfortunately, the fatality also increases. In this research, we propose a pipeline coined as PiXelNet, which uses a classification system based on Convolutional Neural Networks (CNNs) that identifies three distinct kinds of lung cancer on histopathological images. The first step of the proposed network consists of a medical imaging analysis pipeline with models like ResNet, Efficient NetBO and MobileNet. We found that EfficientNet outperforms the other two models with a test accuracy of 99.33% and a loss of 0.0066. The second stage involves identifying the key areas from the original input test image with the feature extracted values. Using this strategy, the doctor or pathologist will immediately access all the crucial imaging heat maps and the network analysis report.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124898868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/acmlc58173.2022.00019
Gaowei Zhou
We propose a prototype- and metric-based prediction method together with several training pipelines suitable for training a network without using any additional data in the few-shot learning tasks with different intra-class variances. Being tested on two datasets commonly used for few-shot learning, our method has shown satisfactory ability to improve data efficiency and prevent overfitting. It even competes with the meta-learning-based method trained with a lot of extra labeled samples on the dataset with low intra-class variance and shows no significant performance gap when it comes to the dataset with a high intra-class variance. We reported 99.0% acc on the Omniglot dataset and 48.0% acc on the mini-ImageNet for 5-way 5-shot tasks.
{"title":"Prototype and Metric Based Prediction for Data-Efficient Training","authors":"Gaowei Zhou","doi":"10.1109/acmlc58173.2022.00019","DOIUrl":"https://doi.org/10.1109/acmlc58173.2022.00019","url":null,"abstract":"We propose a prototype- and metric-based prediction method together with several training pipelines suitable for training a network without using any additional data in the few-shot learning tasks with different intra-class variances. Being tested on two datasets commonly used for few-shot learning, our method has shown satisfactory ability to improve data efficiency and prevent overfitting. It even competes with the meta-learning-based method trained with a lot of extra labeled samples on the dataset with low intra-class variance and shows no significant performance gap when it comes to the dataset with a high intra-class variance. We reported 99.0% acc on the Omniglot dataset and 48.0% acc on the mini-ImageNet for 5-way 5-shot tasks.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127592312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ACMLC58173.2022.00029
Yanru Lv, Junmin Yi
Packing problem and vehicle routing problem have always been the hot issues in the field of logistics and operations research, and they are NP-hard problems. In order to meet the requirements of practical transport and storage, the integrated problem of these two kinds of problems – vehicle routing problem with loading constraints is constantly targeted. Therefore, scholars continue to explore its models and solutions to meet the increasingly complex transport demand in practice. This article reviews the recent literature of vehicle routing problem with loading constraints, including those practically critical loading-related constraints, variation problems due to new constraints or targets. The solution method for vehicle routing problem with loading constraints is also briefly summarized. Furthermore, the problem instances and application are focused, and some research perspectives are proposed finally.
{"title":"On the Instances and Application of Routing Problem with Loading Constraints","authors":"Yanru Lv, Junmin Yi","doi":"10.1109/ACMLC58173.2022.00029","DOIUrl":"https://doi.org/10.1109/ACMLC58173.2022.00029","url":null,"abstract":"Packing problem and vehicle routing problem have always been the hot issues in the field of logistics and operations research, and they are NP-hard problems. In order to meet the requirements of practical transport and storage, the integrated problem of these two kinds of problems – vehicle routing problem with loading constraints is constantly targeted. Therefore, scholars continue to explore its models and solutions to meet the increasingly complex transport demand in practice. This article reviews the recent literature of vehicle routing problem with loading constraints, including those practically critical loading-related constraints, variation problems due to new constraints or targets. The solution method for vehicle routing problem with loading constraints is also briefly summarized. Furthermore, the problem instances and application are focused, and some research perspectives are proposed finally.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127740752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ACMLC58173.2022.00011
Matthias Lermer, Christoph Reich, D. Abdeslam
Evolutionary strategy is increasingly used for optimization in various machine learning problems. It can scale very well, even to high dimensional problems, and its ability to globally self optimize in flexible ways provides new and exciting opportunities when combined with more recent machine learning methods. This paper describes a novel approach for the optimization of models with a data driven evolutionary strategy. The optimization can directly be applied as a preprocessing step and is therefore independent of the machine learning algorithm used. The experimental analysis of six different use cases show that, on average, better results are attained than without evolutionary strategy. Furthermore it is shown, that the best individual models are also achieved with the help of evolutionary strategy. The six different use cases were of different complexity which reinforces the idea that the approach is universal and not depending on specific use cases.
{"title":"An Evolutionary Strategy Based Training Optimization of Supervised Machine Learning Algorithms (EStoTimeSMLAs)","authors":"Matthias Lermer, Christoph Reich, D. Abdeslam","doi":"10.1109/ACMLC58173.2022.00011","DOIUrl":"https://doi.org/10.1109/ACMLC58173.2022.00011","url":null,"abstract":"Evolutionary strategy is increasingly used for optimization in various machine learning problems. It can scale very well, even to high dimensional problems, and its ability to globally self optimize in flexible ways provides new and exciting opportunities when combined with more recent machine learning methods. This paper describes a novel approach for the optimization of models with a data driven evolutionary strategy. The optimization can directly be applied as a preprocessing step and is therefore independent of the machine learning algorithm used. The experimental analysis of six different use cases show that, on average, better results are attained than without evolutionary strategy. Furthermore it is shown, that the best individual models are also achieved with the help of evolutionary strategy. The six different use cases were of different complexity which reinforces the idea that the approach is universal and not depending on specific use cases.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116054217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/acmlc58173.2022.00015
M. Aswath, S. Sowdeshwar, M. Saravanan, Satheesh K. Perepu
Recently, Machine Learning (ML) methods are built-in as an important component in many smart agriculture platforms. In this paper, we explore the new combination of advanced ML methods for creating a smart agriculture platform where farmers could reach out for assistance from the public, or a closed circle of experts. Specifically, we focus on an easy way to assist the farmers in understanding plant diseases where the farmers can get help to solve the issues from the members of the community. The proposed system utilizes deep learning techniques for identifying the disease of the plant from the affected image, which acts as an initial identifier. Further, Natural Language Processing techniques are employed for ranking the solutions posted by the user community. In this paper, a message channel is built on top of Twitter, a popular social media platform to establish proper communication among farmers. Since the effect of the solutions can differ based on various other parameters, we extend the use of the concept drift approach and come up with a good solution and propose it to the farmer. We tested the proposed framework on the benchmark dataset, and it produces accurate and reliable results.
{"title":"Advanced Machine Learning Framework for Efficient Plant Disease Prediction","authors":"M. Aswath, S. Sowdeshwar, M. Saravanan, Satheesh K. Perepu","doi":"10.1109/acmlc58173.2022.00015","DOIUrl":"https://doi.org/10.1109/acmlc58173.2022.00015","url":null,"abstract":"Recently, Machine Learning (ML) methods are built-in as an important component in many smart agriculture platforms. In this paper, we explore the new combination of advanced ML methods for creating a smart agriculture platform where farmers could reach out for assistance from the public, or a closed circle of experts. Specifically, we focus on an easy way to assist the farmers in understanding plant diseases where the farmers can get help to solve the issues from the members of the community. The proposed system utilizes deep learning techniques for identifying the disease of the plant from the affected image, which acts as an initial identifier. Further, Natural Language Processing techniques are employed for ranking the solutions posted by the user community. In this paper, a message channel is built on top of Twitter, a popular social media platform to establish proper communication among farmers. Since the effect of the solutions can differ based on various other parameters, we extend the use of the concept drift approach and come up with a good solution and propose it to the farmer. We tested the proposed framework on the benchmark dataset, and it produces accurate and reliable results.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122844055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ACMLC58173.2022.00018
Yu-Ru Syau, E. Lin
Given a set of objects G with a set of attributes M and a binary relation I, a formal context (G, M, I) corresponds to a Boolean-valued information table in rough set theory, and the kernel relation of the function from G to M which maps each object of G to its afterset. This coincides with the indiscernibility relation of the Boolean-valued information table. Rough set model and Variable-Precision model (VP-model) under a formal context can be formulated based on the associated kernel relation on the objects set. $alpha$-fuzzified rough set model and VP-model under a fuzzy formal context are formulated analogously. We present an example to demonstrate the proposed $alpha$-fuzzified rough set model and VP-model under a fuzzy formal context.
给定一组具有属性M的对象G和一个二元关系I，一个形式上下文(G, M, I)对应于粗糙集理论中的布尔值信息表，以及从G到M的函数的核关系，该函数将G的每个对象映射到它的后集。这与布尔值信息表的不可分辨关系是一致的。基于对象集上的关联核关系，可以建立形式上下文下的粗糙集模型和变精度模型。将$alpha$-模糊粗糙集模型和模糊形式环境下的vp -模型类比地表述出来。我们给出了一个例子来证明在模糊形式环境下提出的$alpha$-模糊粗糙集模型和vp -模型。
{"title":"Rough Set Model and Approximations in Fuzzy Formal Contexts","authors":"Yu-Ru Syau, E. Lin","doi":"10.1109/ACMLC58173.2022.00018","DOIUrl":"https://doi.org/10.1109/ACMLC58173.2022.00018","url":null,"abstract":"Given a set of objects G with a set of attributes M and a binary relation I, a formal context (G, M, I) corresponds to a Boolean-valued information table in rough set theory, and the kernel relation of the function from G to M which maps each object of G to its afterset. This coincides with the indiscernibility relation of the Boolean-valued information table. Rough set model and Variable-Precision model (VP-model) under a formal context can be formulated based on the associated kernel relation on the objects set. $alpha$-fuzzified rough set model and VP-model under a fuzzy formal context are formulated analogously. We present an example to demonstrate the proposed $alpha$-fuzzified rough set model and VP-model under a fuzzy formal context.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133873753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ACMLC58173.2022.00009
Patrick Hosein
An automobile insurance policy premium depends on three factors, the risk associated with the drivers and cars on the policy, the operational costs to manage the policy and the profit margin. The premium is then some function of these. Operational costs are dependent on the company efficiency. The achieved profit margin is dependent on the competition experienced. Risk, however, is a customer dependent factor and hence premiums should take into account potential risk of a new policy. Traditionally, risk tables are used to compute the risk of a new customer but we instead use historical data to predict the average claim amount that would be made on a new policy in the coming year if it was approved. We use this value, as a measure of risk, to better determine the premium that is charged. We illustrate the approach with a single customer feature, the age of the driver, but the approach can be used to take into account several customer and/or car features.
{"title":"A Data-Driven Pricing Strategy for Automobile Insurance Policies","authors":"Patrick Hosein","doi":"10.1109/ACMLC58173.2022.00009","DOIUrl":"https://doi.org/10.1109/ACMLC58173.2022.00009","url":null,"abstract":"An automobile insurance policy premium depends on three factors, the risk associated with the drivers and cars on the policy, the operational costs to manage the policy and the profit margin. The premium is then some function of these. Operational costs are dependent on the company efficiency. The achieved profit margin is dependent on the competition experienced. Risk, however, is a customer dependent factor and hence premiums should take into account potential risk of a new policy. Traditionally, risk tables are used to compute the risk of a new customer but we instead use historical data to predict the average claim amount that would be made on a new policy in the coming year if it was approved. We use this value, as a measure of risk, to better determine the premium that is charged. We illustrate the approach with a single customer feature, the age of the driver, but the approach can be used to take into account several customer and/or car features.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114208806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ACMLC58173.2022.00013
Lihe Ma
Research shows that high turnover rate will inevitably damage the sustainable and healthy development of the enterprise. Thanks to the rapid development of artificial intelligence technology, it is possible to build a model to predict employee turnover intension by analyzing employee turnover data. This study uses employee data of a company on the Kaggle platform, proposes an oversampling method for predicting employee turnover in view of data imbalance in the data set. Four models Gaussian NB, support vector machine for classification (SVC), K-Nearest Neighbor (KNN) and Gradient Boosting were established and trained to analyze the employee turnover features and predict the occurrence of employee turnover events.
{"title":"Employee Turnover Prediction based on Machine Learning Model","authors":"Lihe Ma","doi":"10.1109/ACMLC58173.2022.00013","DOIUrl":"https://doi.org/10.1109/ACMLC58173.2022.00013","url":null,"abstract":"Research shows that high turnover rate will inevitably damage the sustainable and healthy development of the enterprise. Thanks to the rapid development of artificial intelligence technology, it is possible to build a model to predict employee turnover intension by analyzing employee turnover data. This study uses employee data of a company on the Kaggle platform, proposes an oversampling method for predicting employee turnover in view of data imbalance in the data set. Four models Gaussian NB, support vector machine for classification (SVC), K-Nearest Neighbor (KNN) and Gradient Boosting were established and trained to analyze the employee turnover features and predict the occurrence of employee turnover events.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124739570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ACMLC58173.2022.00012
Chenhong Zheng, Mengqian Zhang, Y. Wang, Meihua Zou
How to use automation, optimize the comprehensive budget management system, and help the automatic collection of budget data and budget preparation has become a growing concern for enterprises. This paper combines IT technologies such as robot process automation (PRA) and machine learning algorithm with comprehensive budget management, optimizes the budget data collection process, conducts budget data mining and analysis, so as to help enterprises formulate budget plans, and puts forward implementation suggestions and safeguards.
{"title":"Application of PRA and Machine Learning Algorithm in Budget Data Acquisition and Processing System","authors":"Chenhong Zheng, Mengqian Zhang, Y. Wang, Meihua Zou","doi":"10.1109/ACMLC58173.2022.00012","DOIUrl":"https://doi.org/10.1109/ACMLC58173.2022.00012","url":null,"abstract":"How to use automation, optimize the comprehensive budget management system, and help the automatic collection of budget data and budget preparation has become a growing concern for enterprises. This paper combines IT technologies such as robot process automation (PRA) and machine learning algorithm with comprehensive budget management, optimizes the budget data collection process, conducts budget data mining and analysis, so as to help enterprises formulate budget plans, and puts forward implementation suggestions and safeguards.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"396 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122792846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/ACMLC58173.2022.00026
Lei Zhang, Peng Sun
Travel planning is an important part of tourism. Unlike traditional experience journeys, these journeys developed using mathematical modeling techniques are more scientifically reliable. The mathematical model of travel planning problem is based on tourism marketing problem, which can be solved by ant trap algorithm. At the same time, the development of information technology has led to the transformation of tourism travel organization from the traditional experience based design to a higher level. In this work, this paper focuses on the use of advanced ant algorithm to solve the travel booking problem, self-guided route planning problem and intelligent route planning problem. First, this paper proposes an advanced solution to the ACO based travel assignment problem. In order to realize the ant trap algorithm to solve the travel route problem, when solving the travel quota problem, the ant trap algorithm should obtain the optimal solution with high probability, and the solution time of the algorithm should be relatively short. Secondly, this paper improves the path selection probability and pheromone updating rules, locally searches the optimal path, optimizes the algorithm solving process, and determines the logic parameters of the algorithm. Through performance simulation analysis, the algorithm proposed in this work solves the line problem, with high search accuracy and short solution time.
{"title":"An Optimal Travel Route Optimization Model Based on Ant Colony Optimization Algorithm","authors":"Lei Zhang, Peng Sun","doi":"10.1109/ACMLC58173.2022.00026","DOIUrl":"https://doi.org/10.1109/ACMLC58173.2022.00026","url":null,"abstract":"Travel planning is an important part of tourism. Unlike traditional experience journeys, these journeys developed using mathematical modeling techniques are more scientifically reliable. The mathematical model of travel planning problem is based on tourism marketing problem, which can be solved by ant trap algorithm. At the same time, the development of information technology has led to the transformation of tourism travel organization from the traditional experience based design to a higher level. In this work, this paper focuses on the use of advanced ant algorithm to solve the travel booking problem, self-guided route planning problem and intelligent route planning problem. First, this paper proposes an advanced solution to the ACO based travel assignment problem. In order to realize the ant trap algorithm to solve the travel route problem, when solving the travel quota problem, the ant trap algorithm should obtain the optimal solution with high probability, and the solution time of the algorithm should be relatively short. Secondly, this paper improves the path selection probability and pheromone updating rules, locally searches the optimal path, optimizes the algorithm solving process, and determines the logic parameters of the algorithm. Through performance simulation analysis, the algorithm proposed in this work solves the line problem, with high search accuracy and short solution time.","PeriodicalId":375920,"journal":{"name":"2022 5th Asia Conference on Machine Learning and Computing (ACMLC)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127336939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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