Pub Date : 2024-06-06DOI: 10.19139/soic-2310-5070-1919
Said Benlakhdar, Mohammed Rziza, Rachid Oulad Haj Thami
Multimodal and asymmetric circular data manifest in diverse disciplines, underscoring the significance of fitting suitable distributions for the analysis of such data. This study undertakes a comprehensive comparative assessment, encompassing diverse extensions of the von Mises distribution and the associated statistical methodologies, spanning from Richard von Mises' seminal work in 1918 to contemporary applications, with a particular focus on the field of wind energy. The primary objective is to discern the strengths and limitations inherent in each method. To illustrate the practical implications, three authentic datasets and a simulation study are incorporated to showcase the performance of the proposed models. Furthermore, this paper provides an exhaustive list of references pertinent to von Mises distribution models.
多模态和非对称循环数据体现在不同的学科中，这突出了拟合合适的分布来分析此类数据的重要性。本研究进行了全面的比较评估，涵盖了冯-米塞斯分布的各种扩展和相关统计方法，时间跨度从理查德-冯-米塞斯 1918 年的开创性工作到当代应用，尤其侧重于风能领域。主要目的是辨别每种方法固有的优势和局限性。为了说明实际意义，本文纳入了三个真实数据集和一项模拟研究，以展示所提模型的性能。此外，本文还提供了与 von Mises 分布模型相关的详尽参考文献列表。
{"title":"In-depth Analysis of von Mises Distribution Models: Understanding Theory, Applications, and Future Directions","authors":"Said Benlakhdar, Mohammed Rziza, Rachid Oulad Haj Thami","doi":"10.19139/soic-2310-5070-1919","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1919","url":null,"abstract":"Multimodal and asymmetric circular data manifest in diverse disciplines, underscoring the significance of fitting suitable distributions for the analysis of such data. This study undertakes a comprehensive comparative assessment, encompassing diverse extensions of the von Mises distribution and the associated statistical methodologies, spanning from Richard von Mises' seminal work in 1918 to contemporary applications, with a particular focus on the field of wind energy. The primary objective is to discern the strengths and limitations inherent in each method. To illustrate the practical implications, three authentic datasets and a simulation study are incorporated to showcase the performance of the proposed models. Furthermore, this paper provides an exhaustive list of references pertinent to von Mises distribution models.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"337 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141380899","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 : 2024-06-04DOI: 10.19139/soic-2310-5070-1768
H. Muhammed, E. Muhammed
In this paper, Bayesian and non-Bayesian estimations of the shape parameter of the Inverted Topp-Leone distribution are studied under a progressive Type I censoring scheme. The maximum likelihood estimator (MLE) and Bayes estimator (BE) of the unknown parameter under the squared error loss (SEL) function are obtained. Three types of confidence intervals are discussed for the unknown parameter. A simulation study is performed to compare the performances of the proposed methods, and two numerical examples have been analyzed for illustrative purposes.
本文研究了在渐进 I 型剔除方案下对倒 Topp-Leone 分布形状参数的贝叶斯和非贝叶斯估计。在平方误差损失（SEL）函数下，得到了未知参数的最大似然估计量（MLE）和贝叶斯估计量（BE）。讨论了未知参数的三种置信区间。为比较所提方法的性能，进行了模拟研究，并分析了两个数值示例以作说明。
{"title":"Bayesian and Non-Bayesian Estimation for The Parameter of Inverted Topp-Leone Distribution Based on Progressive Type I Censoring","authors":"H. Muhammed, E. Muhammed","doi":"10.19139/soic-2310-5070-1768","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1768","url":null,"abstract":"In this paper, Bayesian and non-Bayesian estimations of the shape parameter of the Inverted Topp-Leone distribution are studied under a progressive Type I censoring scheme. The maximum likelihood estimator (MLE) and Bayes estimator (BE) of the unknown parameter under the squared error loss (SEL) function are obtained. Three types of confidence intervals are discussed for the unknown parameter. A simulation study is performed to compare the performances of the proposed methods, and two numerical examples have been analyzed for illustrative purposes. ","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"96 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141387272","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 : 2024-02-18DOI: 10.19139/soic-2310-5070-1890
Tanawan Watthaisong, K. Sunat, Nipotepat Muangkote
Dealing with imbalanced data is crucial and challenging when developing effective machine-learning models for data classification purposes. It significantly impacts the classification model's performance without proper data management, leading to suboptimal results. Many methods for managing imbalanced data have been studied and developed to improve data balance. In this paper, we conduct a comparative study to assess the influence of a ranking technique on the evaluation of the effectiveness of 66 traditional methods for addressing imbalanced data. The three classification models, i.e., Decision Tree, Random Forest, and XGBoost, act as classification models. The experimental settings have been divided into two segments. The first part evaluates the performance of various imbalanced dataset handling methods, while the second part compares the performance of the top 4 oversampling methods. The study encompasses 50 separate datasets: 20 retrieved from the UCI repository and 30 sourced from the OpenML repository. The evaluation is based on F-Measure and statistical methods, including the Kruskal-Wallis test and Borda Count, to rank the data imbalance handling capabilities of the 66 methods. The SMOTE technique is the benchmark for comparison due to its popularity in handling imbalanced data. Based on the experimental results, the MCT, Polynom-fit-SMOTE, and CBSO methods were identified as the top three performers, demonstrating superior effectiveness in managing imbalanced datasets. This research could be beneficial and serve as a practical guide for practitioners to apply suitable techniques for data management.
{"title":"Comparative Evaluation of Imbalanced Data Management Techniques for Solving Classification Problems on Imbalanced Datasets","authors":"Tanawan Watthaisong, K. Sunat, Nipotepat Muangkote","doi":"10.19139/soic-2310-5070-1890","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1890","url":null,"abstract":"Dealing with imbalanced data is crucial and challenging when developing effective machine-learning models for data classification purposes. It significantly impacts the classification model's performance without proper data management, leading to suboptimal results. Many methods for managing imbalanced data have been studied and developed to improve data balance. In this paper, we conduct a comparative study to assess the influence of a ranking technique on the evaluation of the effectiveness of 66 traditional methods for addressing imbalanced data. The three classification models, i.e., Decision Tree, Random Forest, and XGBoost, act as classification models. The experimental settings have been divided into two segments. The first part evaluates the performance of various imbalanced dataset handling methods, while the second part compares the performance of the top 4 oversampling methods. The study encompasses 50 separate datasets: 20 retrieved from the UCI repository and 30 sourced from the OpenML repository. The evaluation is based on F-Measure and statistical methods, including the Kruskal-Wallis test and Borda Count, to rank the data imbalance handling capabilities of the 66 methods. The SMOTE technique is the benchmark for comparison due to its popularity in handling imbalanced data. Based on the experimental results, the MCT, Polynom-fit-SMOTE, and CBSO methods were identified as the top three performers, demonstrating superior effectiveness in managing imbalanced datasets. This research could be beneficial and serve as a practical guide for practitioners to apply suitable techniques for data management.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"158 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452528","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 : 2024-02-18DOI: 10.19139/soic-2310-5070-1980
Jinhong Feng, Rui Yan, Gaohang Yu, Zhongming Chen
Analyzing the spectral and spatial characteristics of Hyperspectral Imaging (HSI) in a three-dimensional space is a challenging task. Recently, there have been developments in 3D feature extraction methods based on tensor decomposition, which allow for the effective utilization of both global and local information in HSI. These methods also explore the inherent low-rank properties of HSI through tensor decomposition. In this paper, we propose a new approach called variable randomized T-product decomposition (Vrt-SVD), which is a variation of Tensor Singular Spectral Analysis. The goal of this approach is to improve the efficiency of tensor methods for feature extraction and reduce artifacts of image processing. By using a randomized algorithm based on the variable t-SVD, we are able to capture both global and local spatial and spectral information in HSI efficiently, which enables us to explore its low-rank characteristics. To evaluate the effectiveness of the extracted features, we use a Support Vector Machine (SVM) classifier to assess the accuracy of image classification. By conducting numerous numerical experiments, we provide strong evidence to show that the proposed method outperforms several advanced feature extraction techniques.
{"title":"An Effective Randomized Algorithm for Hyperspectral Image Feature Extraction","authors":"Jinhong Feng, Rui Yan, Gaohang Yu, Zhongming Chen","doi":"10.19139/soic-2310-5070-1980","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1980","url":null,"abstract":"Analyzing the spectral and spatial characteristics of Hyperspectral Imaging (HSI) in a three-dimensional space is a challenging task. Recently, there have been developments in 3D feature extraction methods based on tensor decomposition, which allow for the effective utilization of both global and local information in HSI. These methods also explore the inherent low-rank properties of HSI through tensor decomposition. In this paper, we propose a new approach called variable randomized T-product decomposition (Vrt-SVD), which is a variation of Tensor Singular Spectral Analysis. The goal of this approach is to improve the efficiency of tensor methods for feature extraction and reduce artifacts of image processing. By using a randomized algorithm based on the variable t-SVD, we are able to capture both global and local spatial and spectral information in HSI efficiently, which enables us to explore its low-rank characteristics. To evaluate the effectiveness of the extracted features, we use a Support Vector Machine (SVM) classifier to assess the accuracy of image classification. By conducting numerous numerical experiments, we provide strong evidence to show that the proposed method outperforms several advanced feature extraction techniques.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"42 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452988","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 : 2024-02-18DOI: 10.19139/soic-2310-5070-1399
Katia Hassaini, Mohand Ouamer Bibi
In this study, we propose an approach for solving a quadraticprogramming problem with an M-matrix and simple constraints (QPs). It isbased on the algorithms of Luk-Pagano and Stachurski. These methods usethe fact that an M-matrix possesses a nonnegative inverse which allows tohave a sequence of feasible points monotonically increasing. Introducing theconcept of support for an objective function developed by Gabasov et al., ourapproach leads to a more general condition which allows to have an initialfeasible solution, related to a coordinator support and close to the optimalsolution. The programming under MATLAB of our method and that of Lukand Pagano has allowed us to make a comparison between them, with anillustration on two numerical examples.
在本研究中，我们提出了一种解决具有 M 矩阵和简单约束条件（QPs）的二次编程问题的方法。该方法基于 Luk-Pagano 和 Stachurski 的算法。这些方法利用了一个事实，即 M 矩阵具有一个非负倒数，它允许可行点序列单调递增。通过引入 Gabasov 等人提出的目标函数支持概念，我们的方法得出了一个更普遍的条件，即允许有一个与协调支持相关且接近最优解的初始可行解。通过在 MATLAB 中对我们的方法和 Lukand Pagano 的方法进行编程，我们可以对它们进行比较，并通过两个数值示例进行说明。
{"title":"An Algorithm for Solving Quadratic Programming Problems with an M-matrix","authors":"Katia Hassaini, Mohand Ouamer Bibi","doi":"10.19139/soic-2310-5070-1399","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1399","url":null,"abstract":"In this study, we propose an approach for solving a quadraticprogramming problem with an M-matrix and simple constraints (QPs). It isbased on the algorithms of Luk-Pagano and Stachurski. These methods usethe fact that an M-matrix possesses a nonnegative inverse which allows tohave a sequence of feasible points monotonically increasing. Introducing theconcept of support for an objective function developed by Gabasov et al., ourapproach leads to a more general condition which allows to have an initialfeasible solution, related to a coordinator support and close to the optimalsolution. The programming under MATLAB of our method and that of Lukand Pagano has allowed us to make a comparison between them, with anillustration on two numerical examples.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"205 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140452673","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 : 2024-02-17DOI: 10.19139/soic-2310-5070-1721
Simbarashe Chamunorwa, B. Oluyede, Thatayone Moakofi, Fastel Chipepa
We propose and study a new generalized class of distributions called the Type II Exponentiated Half Logistic-Gompertz-G Power Series (TIIEHL-Gom-GPS) distribution. Some structural properties including expansion of density,ordinary and conditional moments, generating function, order statistics and entropy are derived. We present some specialcases of the proposed distribution. The maximum likelihood method is used for estimating the model parameters. Theimportance of the new class of distributions are illustrated by means of two applications to real data sets.
我们提出并研究了一类新的广义分布，即 II 型幂级数半对数-贡珀兹-幂级数分布（TIIEHL-Gom-GPS）。我们推导了一些结构性质，包括密度扩展、普通矩和条件矩、生成函数、阶次统计量和熵。我们介绍了拟议分布的一些特例。最大似然法用于估计模型参数。通过对真实数据集的两个应用，说明了这一类新分布的重要性。
{"title":"The Type II Exponentiated Half Logistic-Gompertz-G Power Series Class of Distributions: Properties and Applications","authors":"Simbarashe Chamunorwa, B. Oluyede, Thatayone Moakofi, Fastel Chipepa","doi":"10.19139/soic-2310-5070-1721","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1721","url":null,"abstract":"We propose and study a new generalized class of distributions called the Type II Exponentiated Half Logistic-Gompertz-G Power Series (TIIEHL-Gom-GPS) distribution. Some structural properties including expansion of density,ordinary and conditional moments, generating function, order statistics and entropy are derived. We present some specialcases of the proposed distribution. The maximum likelihood method is used for estimating the model parameters. Theimportance of the new class of distributions are illustrated by means of two applications to real data sets.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"134 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140453087","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 : 2024-02-17DOI: 10.19139/soic-2310-5070-1836
F. Abdullayeva, Suleyman Suleymanzade
In this paper, we propose to use a convertible deep neural network (DNN) model with a transfer adaptation mechanism to deal with varying input and output numbers of neurons. The flexible DNN model serves as a multi-label classifier for the recommender system as part of the retrieval systems’ push mechanism, which learns the combination of tabular features and proposes the number of discrete offers (targets). Our retrieval system uses the transfer adaptation, mechanism, when the number of features changes, it replaces the input layer of the neural network then freezes all gradients on the following layers, trains only replaced layer, and unfreezes the entire model. The experiments show that using the transfer adaptation technique impacts stable loss decreasing and learning speed during the training process.
{"title":"Using transfer adaptation method for dynamic features expansion in multi-label deep neural network for recommender systems","authors":"F. Abdullayeva, Suleyman Suleymanzade","doi":"10.19139/soic-2310-5070-1836","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1836","url":null,"abstract":"In this paper, we propose to use a convertible deep neural network (DNN) model with a transfer adaptation mechanism to deal with varying input and output numbers of neurons. The flexible DNN model serves as a multi-label classifier for the recommender system as part of the retrieval systems’ push mechanism, which learns the combination of tabular features and proposes the number of discrete offers (targets). Our retrieval system uses the transfer adaptation, mechanism, when the number of features changes, it replaces the input layer of the neural network then freezes all gradients on the following layers, trains only replaced layer, and unfreezes the entire model. The experiments show that using the transfer adaptation technique impacts stable loss decreasing and learning speed during the training process. \u0000 \u0000","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"143 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140453225","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 : 2024-01-29DOI: 10.19139/soic-2310-5070-1870
Hanaa Hussein Ali
This study aims to estimate the relationship between the Protection of intellectual property rights indices and the foreign trade index in Egypt from 1995 to 2022. The comparison has been made between many models such as full modified ordinary least squares (FMOLS) model, dynamic ordinary least squares (DOLS) model , canonical co-integration regression (CCR) model and autoregressive distributed lag (ARDL) model. The results of the study showed that the best model was the ARDL model to increase its interpretive capacity. The study also showed that the most important property rights protection indicators affecting the foreign trade index are the number of applications and registrations of brands, the number of patents registered and granted, the number of applications and registrations of industrial designs, and the proportion of expenditure on research and development as a proportion of gross domestic product (GDP). The estimated model also passed all diagnostic tests and showed that there was no autocorrelation, no Heteroskedasticity. In addition, it was found to follow a normal distribution and to be stable.
{"title":"Statistical Models to Measure the Impact of Intellectual Property Rights Protection on Foreign Trade in Egypt","authors":"Hanaa Hussein Ali","doi":"10.19139/soic-2310-5070-1870","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1870","url":null,"abstract":"This study aims to estimate the relationship between the Protection of intellectual property rights indices and the foreign trade index in Egypt from 1995 to 2022. The comparison has been made between many models such as full modified ordinary least squares (FMOLS) model, dynamic ordinary least squares (DOLS) model , canonical co-integration regression (CCR) model and autoregressive distributed lag (ARDL) model. The results of the study showed that the best model was the ARDL model to increase its interpretive capacity. The study also showed that the most important property rights protection indicators affecting the foreign trade index are the number of applications and registrations of brands, the number of patents registered and granted, the number of applications and registrations of industrial designs, and the proportion of expenditure on research and development as a proportion of gross domestic product (GDP). The estimated model also passed all diagnostic tests and showed that there was no autocorrelation, no Heteroskedasticity. In addition, it was found to follow a normal distribution and to be stable.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"40 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140488944","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 : 2024-01-29DOI: 10.19139/soic-2310-5070-1880
Nour El houda Djemoui, A. Chadli, Ilhem Merah
In this work, we investigate the estimation of the unknown parameters and the reliability characteristics ofthe Odd Lindley Exponentiated Exponential distribution. The Bayes estimators and corresponding risks are derived usingvarious loss functions with complete data and a gamma prior distribution. A simulation study was carried out to calculate allthe results. We used Pitman’s closeness criterion and the integrated mean squared error to compare the performance of theBayesian and maximum likelihood estimators. Finally, we illustrate our techniques by analysing a real-life data set.
{"title":"Bayesian Estimation of the Odd Lindley Exponentiated Exponential Distribution : Applications in-Reliability","authors":"Nour El houda Djemoui, A. Chadli, Ilhem Merah","doi":"10.19139/soic-2310-5070-1880","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1880","url":null,"abstract":" In this work, we investigate the estimation of the unknown parameters and the reliability characteristics ofthe Odd Lindley Exponentiated Exponential distribution. The Bayes estimators and corresponding risks are derived usingvarious loss functions with complete data and a gamma prior distribution. A simulation study was carried out to calculate allthe results. We used Pitman’s closeness criterion and the integrated mean squared error to compare the performance of theBayesian and maximum likelihood estimators. Finally, we illustrate our techniques by analysing a real-life data set.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"58 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140486632","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 : 2024-01-17DOI: 10.19139/soic-2310-5070-1876
Mohamed Abd, Allah El-Hadidy, M. Fakharany
This paper presents a new probabilistic coordinated search technique for finding a randomly located target in n-disjoint known regions by using n-searchers. Each region contains one searcher. The searchers use advanced technology to communicate with each other. The purpose of this paper is to obtain the candidate utility function namely the expected value of the time for detecting the target. Additionally, to minimize this expected value given a restricted amount of time. We present a special case when the target has a multinomial distribution. This important for searching about a valuable target missing at sea or lost at wilderness area.
本文提出了一种新的概率协调搜索技术，利用 n 个搜索者在 n 个不相邻的已知区域中寻找随机定位的目标。每个区域包含一名搜索者。搜索者之间使用先进技术进行通信。本文的目的是获得候选效用函数，即检测目标时间的期望值。此外，还要在时间有限的情况下最大限度地减少这一期望值。我们提出了目标具有多叉分布的一种特殊情况。这对于搜寻在海上失踪或在荒野地区丢失的有价值目标非常重要。
{"title":"On Probabilistic Cooperative Search Model to Detect a Lost Target in N-Disjoint Areas","authors":"Mohamed Abd, Allah El-Hadidy, M. Fakharany","doi":"10.19139/soic-2310-5070-1876","DOIUrl":"https://doi.org/10.19139/soic-2310-5070-1876","url":null,"abstract":"This paper presents a new probabilistic coordinated search technique for finding a randomly located target in n-disjoint known regions by using n-searchers. Each region contains one searcher. The searchers use advanced technology to communicate with each other. The purpose of this paper is to obtain the candidate utility function namely the expected value of the time for detecting the target. Additionally, to minimize this expected value given a restricted amount of time. We present a special case when the target has a multinomial distribution. This important for searching about a valuable target missing at sea or lost at wilderness area.","PeriodicalId":131002,"journal":{"name":"Statistics, Optimization & Information Computing","volume":"40 9-10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140505550","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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