Pub Date : 2023-12-22DOI: 10.3103/s1060992x23040094
I. M. Karandashev, G. S. Teplov, A. A. Karmanov, V. V. Keremet, A. V. Kuzovkov
Abstract
The paper deals with the inverse problem of computational lithography. We turn to deep neural network algorithms to compute photomask topologies. The chief goal of the research is to understand how efficient the neural net architectures such as U-net, Erf-Net and Deep Lab v.3, as well as built-in Calibre Workbench algorithms, can be in tackling inverse lithography problems. Specially generated and marked data sets are used to train the artificial neural nets. Calibre EDA software is used to generate haphazard patterns for a 90 nm transistor gate mask. The accuracy and speed parameters are used for the comparison. The edge placement error (EPE) and intersection over union (IOU) are used as metrics. The use of the neural nets allows two orders of magnitude reduction of the mask computation time, with accuracy keeping to 92% for the IOU metric.
{"title":"Investigating the Efficiency of Using U-Net, Erf-Net and DeepLabV3 Architectures in Inverse Lithography-based 90-nm Photomask Generation","authors":"I. M. Karandashev, G. S. Teplov, A. A. Karmanov, V. V. Keremet, A. V. Kuzovkov","doi":"10.3103/s1060992x23040094","DOIUrl":"https://doi.org/10.3103/s1060992x23040094","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The paper deals with the inverse problem of computational lithography. We turn to deep neural network algorithms to compute photomask topologies. The chief goal of the research is to understand how efficient the neural net architectures such as U-net, Erf-Net and Deep Lab v.3, as well as built-in Calibre Workbench algorithms, can be in tackling inverse lithography problems. Specially generated and marked data sets are used to train the artificial neural nets. Calibre EDA software is used to generate haphazard patterns for a 90 nm transistor gate mask. The accuracy and speed parameters are used for the comparison. The edge placement error (EPE) and intersection over union (IOU) are used as metrics. The use of the neural nets allows two orders of magnitude reduction of the mask computation time, with accuracy keeping to 92% for the IOU metric.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029348","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 : 2023-12-22DOI: 10.3103/s1060992x23040100
Raj Kumar, Anuradha Chug, Amit Prakash Singh
Abstract
The Precise and prompt identification of plant pathogens is essential to keep agricultural losses as low as possible. In recent time, deep convolution neural networks have seen an exponential growth in their use in phytopathology due to its capacity for rapid and precise disease identification. However, deep convolutional neural network needs a lot of processing power because of its intricate structure consisting of a large stack of layers and millions of trainable parameters which makes them inedquate for light computing devices. In this article, authors have introduced a novel light-weight sequential CNN architecture for the diagnosis of leaf diseases. The suggested CNN approach contains fewer layers and around 70% less attributes than pre-trained CNN-based approaches. For the experiments and performance evaluation, authors have chosen a benchmark public dataset consisting of 7012 images of tomato and potato leaves affected with early and late blight diseases. The performance of the proposed architecture is compared against three recent priorly trained CNN architectures such as ResNet-50, VGG-16 and MobileNet-V2. The average accuracy percentage reported by the proposed architecture is 98.02 and the time consumed in training is also much better than the existing priorly trained CNN architectures. The experimental findings clearly demonstrate that the suggested approach outperforms the recent existing trained CNN approaches and has a very less number of layers and parameters which significantly reduces the amount of computing resources and time to train the model which could be a better choice for mobile-based real-time plant disease diagnosis applications.
{"title":"Plant Foliage Disease Diagnosis Using Light-Weight Efficient Sequential CNN Model","authors":"Raj Kumar, Anuradha Chug, Amit Prakash Singh","doi":"10.3103/s1060992x23040100","DOIUrl":"https://doi.org/10.3103/s1060992x23040100","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The Precise and prompt identification of plant pathogens is essential to keep agricultural losses as low as possible. In recent time, deep convolution neural networks have seen an exponential growth in their use in phytopathology due to its capacity for rapid and precise disease identification. However, deep convolutional neural network needs a lot of processing power because of its intricate structure consisting of a large stack of layers and millions of trainable parameters which makes them inedquate for light computing devices. In this article, authors have introduced a novel light-weight sequential CNN architecture for the diagnosis of leaf diseases. The suggested CNN approach contains fewer layers and around 70% less attributes than pre-trained CNN-based approaches. For the experiments and performance evaluation, authors have chosen a benchmark public dataset consisting of 7012 images of tomato and potato leaves affected with early and late blight diseases. The performance of the proposed architecture is compared against three recent priorly trained CNN architectures such as ResNet-50, VGG-16 and MobileNet-V2. The average accuracy percentage reported by the proposed architecture is 98.02 and the time consumed in training is also much better than the existing priorly trained CNN architectures. The experimental findings clearly demonstrate that the suggested approach outperforms the recent existing trained CNN approaches and has a very less number of layers and parameters which significantly reduces the amount of computing resources and time to train the model which could be a better choice for mobile-based real-time plant disease diagnosis applications.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029382","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 : 2023-12-22DOI: 10.3103/s1060992x23040033
Shailandra Kumar Prasad, Mrityunjay Kumar Sinha
Abstract
In the chemical, pharmaceutical, and petroleum industries, Shell and U-Tube Heat Exchangers (STHX) were extensively utilized. Baffles must be positioned at the right distance and angle to increase the heat exchangers' capacity to convey heat and, as a result, lower pressure in the shell. The rate of heat transfer in an STHX has been improved, and pressure drop has been reduced using a variety of models. But those methods are not provided satisfactory pressure drop reduction. In the proposed model, an optimal Unilateral Ladder-Type Helical Baffles (ULHB) design and intelligent performance prediction system based U-tube heat exchanger was designed to reduce the pressure drop as well as predict the heat exchanger performance. The shell and tubes were made up of steel and copper material, respectively. A baffle was placed above tubes to barrier the flow of cold water. The design of the baffle was accomplished by using Chimp Optimization Algorithm (ChOA) and is motivated by the hunting behaviour of chimpanzees. After designing the exchanger, its fluid analysis was verified, and the parameter values of the heat exchanger were collected to create a dataset. Based on that data, the intelligent performance prediction-system was designed. The controlling system analysed the given data to predict the performance of the heat exchanger. The suggested model has a pressure drop of 55 Pa, a heat transfer coefficient of 411 U, and 86% accuracy for the thermal performance prediction process. The proposed model provides better performance by improving heat transfer efficiency and significantly reduces pressure drop.
摘要 在化学、制药和石油工业中，壳管和 U 型管热交换器（STHX）得到了广泛应用。挡板必须保持适当的距离和角度，以提高热交换器的传热能力，从而降低壳体内的压力。STHX 热交换器的传热速度得到了提高，压降也通过各种模型得到了降低。但这些方法并不能令人满意地降低压降。在所提出的模型中，设计了一种基于 U 型管换热器的最优单侧阶梯式螺旋挡板（ULHB）设计和智能性能预测系统，以降低压降并预测换热器的性能。壳体和管子分别由钢和铜材料制成。管子上方设有挡板，以阻挡冷水的流动。挡板的设计采用了黑猩猩优化算法（ChOA），其灵感来自黑猩猩的狩猎行为。设计完热交换器后，对其流体分析进行了验证，并收集了热交换器的参数值以创建数据集。根据这些数据，设计了智能性能预测系统。控制系统通过分析给定数据来预测热交换器的性能。建议的模型压降为 55 Pa，传热系数为 411 U，热性能预测准确率为 86%。所建议的模型通过提高传热效率和显著降低压降来提供更好的性能。
{"title":"Lessen Pressure Drop and Forecasting Thermal Performance in U-Tube Heat Exchanger Using Chimp Optimization and Deep Belief Neural Network","authors":"Shailandra Kumar Prasad, Mrityunjay Kumar Sinha","doi":"10.3103/s1060992x23040033","DOIUrl":"https://doi.org/10.3103/s1060992x23040033","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>In the chemical, pharmaceutical, and petroleum industries, Shell and U-Tube Heat Exchangers (STHX) were extensively utilized. Baffles must be positioned at the right distance and angle to increase the heat exchangers' capacity to convey heat and, as a result, lower pressure in the shell. The rate of heat transfer in an STHX has been improved, and pressure drop has been reduced using a variety of models. But those methods are not provided satisfactory pressure drop reduction. In the proposed model, an optimal Unilateral Ladder-Type Helical Baffles (ULHB) design and intelligent performance prediction system based U-tube heat exchanger was designed to reduce the pressure drop as well as predict the heat exchanger performance. The shell and tubes were made up of steel and copper material, respectively. A baffle was placed above tubes to barrier the flow of cold water. The design of the baffle was accomplished by using Chimp Optimization Algorithm (ChOA) and is motivated by the hunting behaviour of chimpanzees. After designing the exchanger, its fluid analysis was verified, and the parameter values of the heat exchanger were collected to create a dataset. Based on that data, the intelligent performance prediction-system was designed. The controlling system analysed the given data to predict the performance of the heat exchanger. The suggested model has a pressure drop of 55 Pa, a heat transfer coefficient of 411 <i>U,</i> and 86% accuracy for the thermal performance prediction process. The proposed model provides better performance by improving heat transfer efficiency and significantly reduces pressure drop.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029384","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 : 2023-12-22DOI: 10.3103/s1060992x23040112
P. Venkatasaichandrakanth, M. Iyapparaja
Abstract
Agronomic pests cause agriculture to incur financial losses because they diminish production, which lowers revenue. Pest control, essential to lowering these losses, involves identifying and eliminating this risk. Since it enables management to take place, identification is the fundamental component of control. Utilizing the pest’s traits, visual identification is done. These characteristics differ between animals and are intrinsic. Since identification is so difficult, specialists in the field handle most of the work, which concentrates the information. Researchers have developed various techniques for predicting crop diseases using images of infected leaves. While progress has been made in identifying plant diseases using different models and methods, new advancements and discussions still offer room for improvement. Technology can significantly improve global crop production, and large datasets can be used to train models and approaches that uncover new and improved methods for detecting plant diseases and addressing low-yield issues. The effectiveness of machine learning and deep learning for identifying and categorizing pests has been confirmed by prior research. This paper thoroughly examines and critically evaluates the many strategies and methodologies used to classify and detect pests or insects using deep learning. The paper examines the benefits and drawbacks of various methodologies and considers potential problems with insect detection via image processing. The paper concludes by providing an analysis and outlook on the future direction of pest detection and classification using deep learning on plants like peanuts.
{"title":"Review on Pest Detection and Classification in Agricultural Environments Using Image-Based Deep Learning Models and Its Challenges","authors":"P. Venkatasaichandrakanth, M. Iyapparaja","doi":"10.3103/s1060992x23040112","DOIUrl":"https://doi.org/10.3103/s1060992x23040112","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>Agronomic pests cause agriculture to incur financial losses because they diminish production, which lowers revenue. Pest control, essential to lowering these losses, involves identifying and eliminating this risk. Since it enables management to take place, identification is the fundamental component of control. Utilizing the pest’s traits, visual identification is done. These characteristics differ between animals and are intrinsic. Since identification is so difficult, specialists in the field handle most of the work, which concentrates the information. Researchers have developed various techniques for predicting crop diseases using images of infected leaves. While progress has been made in identifying plant diseases using different models and methods, new advancements and discussions still offer room for improvement. Technology can significantly improve global crop production, and large datasets can be used to train models and approaches that uncover new and improved methods for detecting plant diseases and addressing low-yield issues. The effectiveness of machine learning and deep learning for identifying and categorizing pests has been confirmed by prior research. This paper thoroughly examines and critically evaluates the many strategies and methodologies used to classify and detect pests or insects using deep learning. The paper examines the benefits and drawbacks of various methodologies and considers potential problems with insect detection via image processing. The paper concludes by providing an analysis and outlook on the future direction of pest detection and classification using deep learning on plants like peanuts.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029377","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 : 2023-12-01DOI: 10.3103/s1060992x23040070
Abstract
Segmenting nuclei from histopathology images is a crucial step in the early identification and diagnosis of several diseases. Due to the complexity of histopathology images, accurate nucleus segmentation is not a simple operation. However, convolutional neural networks (CNNs) have recently been revealed to be a viable option. The well-known CNN model, namely the U-Net, demonstrated its image segmentation effectiveness in medical field. However, U-Net has several drawbacks, such as information loss after transmission through particular steps. Another significant one is the likelihood of feature mismatches in the encoder and decoder sub-networks in skip connection, which can lead to the fusing of semantically unrelated information and, as a consequence, fuzzy feature maps throughout the learning process. In order to solve these issues, an improved U-Net architecture called Information Added U-Net with Sharp Block (IASB-U-Net) has been proposed for nuclei segmentation from histopathology images. Information is added to the encoder-decoder path in the proposed model after each layer, and sharpening spatial filters are utilized in place of skip connections. The experimental study over a merged dataset demonstrates that the proposed IASB-U-Net produces competitive results when compared to established CNN models such as U-Net, Dense U-Net, SCPP Net, and LiverNet.
{"title":"Information Added U-Net with Sharp Block for Nucleus Segmentation of Histopathology Images","authors":"","doi":"10.3103/s1060992x23040070","DOIUrl":"https://doi.org/10.3103/s1060992x23040070","url":null,"abstract":"<span> <h3>Abstract</h3> <p>Segmenting nuclei from histopathology images is a crucial step in the early identification and diagnosis of several diseases. Due to the complexity of histopathology images, accurate nucleus segmentation is not a simple operation. However, convolutional neural networks (CNNs) have recently been revealed to be a viable option. The well-known CNN model, namely the U-Net, demonstrated its image segmentation effectiveness in medical field. However, U-Net has several drawbacks, such as information loss after transmission through particular steps. Another significant one is the likelihood of feature mismatches in the encoder and decoder sub-networks in skip connection, which can lead to the fusing of semantically unrelated information and, as a consequence, fuzzy feature maps throughout the learning process. In order to solve these issues, an improved U-Net architecture called Information Added U-Net with Sharp Block (IASB-U-Net) has been proposed for nuclei segmentation from histopathology images. Information is added to the encoder-decoder path in the proposed model after each layer, and sharpening spatial filters are utilized in place of skip connections. The experimental study over a merged dataset demonstrates that the proposed IASB-U-Net produces competitive results when compared to established CNN models such as U-Net, Dense U-Net, SCPP Net, and LiverNet.</p> </span>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029215","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 : 2023-12-01DOI: 10.3103/s1060992x23040021
M. V. Gashnikov
{"title":"Video Codec Using Machine Learning Based on Parametric Orthogonal Filters","authors":"M. V. Gashnikov","doi":"10.3103/s1060992x23040021","DOIUrl":"https://doi.org/10.3103/s1060992x23040021","url":null,"abstract":"","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139023715","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 : 2023-12-01DOI: 10.3103/s1060992x23040045
Changgeng Yu, Dashi Lin, Chaowen He
{"title":"ASE-UNet: An Orange Fruit Segmentation Model in an Agricultural Environment Based on Deep Learning","authors":"Changgeng Yu, Dashi Lin, Chaowen He","doi":"10.3103/s1060992x23040045","DOIUrl":"https://doi.org/10.3103/s1060992x23040045","url":null,"abstract":"","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138988825","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 : 2023-12-01DOI: 10.3103/s1060992x23040069
O. Angelsky, A. Bekshaev, C. Zenkova, D. Ivanskyi, P. Maksymyak, V. Kryvetsky, Zhebo Chen
{"title":"Application of the Luminescent Carbon Nanoparticles for Optical Diagnostics of Structure-Inhomogeneous Objects at the Micro- and Nanoscales","authors":"O. Angelsky, A. Bekshaev, C. Zenkova, D. Ivanskyi, P. Maksymyak, V. Kryvetsky, Zhebo Chen","doi":"10.3103/s1060992x23040069","DOIUrl":"https://doi.org/10.3103/s1060992x23040069","url":null,"abstract":"","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139015709","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 : 2023-12-01DOI: 10.3103/s1060992x23040124
S. Ye, R. Bohush, H. Chen, S. Ihnatsyeva, S. Ablameyko
{"title":"Data Augmentation and Fine Tuning of Convolutional Neural Network during Training for Person Re-Identification in Video Surveillance Systems","authors":"S. Ye, R. Bohush, H. Chen, S. Ihnatsyeva, S. Ablameyko","doi":"10.3103/s1060992x23040124","DOIUrl":"https://doi.org/10.3103/s1060992x23040124","url":null,"abstract":"","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139015710","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 : 2023-12-01DOI: 10.3103/s1060992x23040082
Abstract
Road traffic accidents are considered a significant problem which ruins the life of many people and also causes major economic losses. So, this issue is considered a hot research topic, and many researchers all over the world are focusing on developing a solution to this most challenging problem. Traditionally the accident spots are detected by means of transportation experts, and following that, some of the statistical models such as linear and nonlinear regression were used for accident severity prediction. However, these traditional approaches do not have the capability to analyze the relationship between the influential factor and accident severity. To address this issue, an Artificial Neural Network (ANN) classifier based vulnerable accident prediction model is proposed in this current research. Initially, the past accident data over the past period of years is collected from a specified area. The acquired data consists of a variable factor related to road infrastructure, weather condition, area of the accident, type of injury and driving characteristics. Then, to standardize the raw input data, min-max normalization is used as a pre-processing technique. The pre-processed is sent for the feature selection process in which essential features are selected by correlating the variable factor with accident severity prediction. Following that, the dimension of the features is reduced using Latent Sematic Index (LSI). Finally, the reduced features are fetched into the ANN classifier for predicting the severity of accidents such as low, medium and high. Simulation analysis of the proposed accident prediction model is carried out by evaluating some of the performance metrics for three datasets. Accuracy, error, specificity, recall and precision attained for the proposed model using dataset 1 is 96.3, 0.03, 98 and 98%. Through this proposed vulnerable accident prediction model, the severity of accidents can be analyzed effectively, and road safety levels can be improved.
摘要 道路交通事故被认为是一个重大问题，它毁掉了许多人的生命，也造成了重大的经济损失。因此，这个问题被认为是一个热门研究课题，世界各地的许多研究人员都在关注如何解决这个最具挑战性的问题。传统上，事故点是通过交通专家来检测的，之后，一些统计模型（如线性和非线性回归）被用于事故严重性预测。然而，这些传统方法无法分析影响因素与事故严重性之间的关系。针对这一问题，本研究提出了一种基于人工神经网络（ANN）分类器的易损事故预测模型。首先，从指定区域收集过去几年的事故数据。获取的数据包括与道路基础设施、天气状况、事故发生区域、伤害类型和驾驶特征相关的可变因素。然后，为了使原始输入数据标准化，使用了最小-最大归一化作为预处理技术。预处理后的数据将被送往特征选择过程，在此过程中，通过将可变因素与事故严重性预测相关联来选择基本特征。然后，使用潜在语义索引（LSI）降低特征的维度。最后，将缩减后的特征提取到 ANN 分类器中，用于预测事故的严重程度，如低、中和高。通过评估三个数据集的一些性能指标，对所提出的事故预测模型进行了仿真分析。在数据集 1 中，所提模型的准确率、误差、特异性、召回率和精确率分别为 96.3%、0.03%、98% 和 98%。通过所提出的易损事故预测模型，可以有效地分析事故的严重程度，提高道路安全水平。
{"title":"Development of Prediction Models for Vulnerable Road User Accident Severity","authors":"","doi":"10.3103/s1060992x23040082","DOIUrl":"https://doi.org/10.3103/s1060992x23040082","url":null,"abstract":"<span> <h3>Abstract</h3> <p>Road traffic accidents are considered a significant problem which ruins the life of many people and also causes major economic losses. So, this issue is considered a hot research topic, and many researchers all over the world are focusing on developing a solution to this most challenging problem. Traditionally the accident spots are detected by means of transportation experts, and following that, some of the statistical models such as linear and nonlinear regression were used for accident severity prediction. However, these traditional approaches do not have the capability to analyze the relationship between the influential factor and accident severity. To address this issue, an Artificial Neural Network (ANN) classifier based vulnerable accident prediction model is proposed in this current research. Initially, the past accident data over the past period of years is collected from a specified area. The acquired data consists of a variable factor related to road infrastructure, weather condition, area of the accident, type of injury and driving characteristics. Then, to standardize the raw input data, min-max normalization is used as a pre-processing technique. The pre-processed is sent for the feature selection process in which essential features are selected by correlating the variable factor with accident severity prediction. Following that, the dimension of the features is reduced using Latent Sematic Index (LSI). Finally, the reduced features are fetched into the ANN classifier for predicting the severity of accidents such as low, medium and high. Simulation analysis of the proposed accident prediction model is carried out by evaluating some of the performance metrics for three datasets. Accuracy, error, specificity, recall and precision attained for the proposed model using dataset 1 is 96.3, 0.03, 98 and 98%. Through this proposed vulnerable accident prediction model, the severity of accidents can be analyzed effectively, and road safety levels can be improved.</p> </span>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":null,"pages":null},"PeriodicalIF":0.9,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139029350","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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