JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES最新文献_第5页

Forecasting stochastic consumer portability visitation pattern in fair price shops of India 印度平价商店消费者可携性随机访问模式预测

IF 1.4

JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES Pub Date : 2023-01-01 DOI: 10.47974/jios-1364

A. Sasi, Thiruselvan Subramanian

引用次数: 0

Enhancing the accuracy of breast cancer detection and determination of risk factor by using the backpropagation network theory and SVM: Machine learning 利用反向传播网络理论和支持向量机:机器学习提高乳腺癌检测和确定危险因素的准确性

IF 1.4

JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES Pub Date : 2023-01-01 DOI: 10.47974/jios-1365

N. Madhavi, Sushil Dohare, G. Prasad, D. Babu, Abdul Rahman Mohammed Al-Ansari

引用次数: 0

Effective negative triplet sampling for knowledge graph embedding 知识图嵌入的有效负三元组采样

IF 1.4

JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES Pub Date : 2022-11-17 DOI: 10.1080/02522667.2022.2133215

A. Khobragade, Rushikesh Mahajan, Hrithik Langi, Rohit Mundhe, S. Ghumbre

{"title":"Effective negative triplet sampling for knowledge graph embedding","authors":"A. Khobragade, Rushikesh Mahajan, Hrithik Langi, Rohit Mundhe, S. Ghumbre","doi":"10.1080/02522667.2022.2133215","DOIUrl":"https://doi.org/10.1080/02522667.2022.2133215","url":null,"abstract":"Abstract Knowledge graphs contain only positive triplet facts, whereas the negative triplets need to be generated precisely to train the embedding models. Early Uniform and Bernoulli sampling are applied but suffer’s from the zero loss problems during training, affecting the performance of embedding models. Recently, generative adversarial technic attended the dynamic negative sampling and obtained better performance by vanishing zero loss but on the adverse side of increasing the model complexity and training parameter. However, NSCaching balances the performance and complexity, generating a single negative triplet sample for each positive triplet that focuses on vanishing gradients. This paper addressed the zero loss training problem due to the low-scored negative triplet by proposing the extended version of NSCaching, to generate the high-scored negative triplet utilized to increase the training performance. The proposed method experimented with semantic matching knowledge graph embedding models on the benchmark datasets, where the results show the success on all evaluation metrics.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"43 1","pages":"2075 - 2087"},"PeriodicalIF":1.4,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45446962","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}

引用次数: 1

A study of feature selection methods for android malware detection 安卓恶意软件检测的特征选择方法研究

IF 1.4

JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES Pub Date : 2022-11-17 DOI: 10.1080/02522667.2022.2133218

D. Kshirsagar, Pooja Agrawal

引用次数: 1

Hyperspectral image classification using meta-heuristics and artificial neural network 基于元启发式和人工神经网络的高光谱图像分类

IF 1.4

JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES Pub Date : 2022-11-17 DOI: 10.1080/02522667.2022.2133222

Sakshi Dhingra, Dharminder Kumar

{"title":"Hyperspectral image classification using meta-heuristics and artificial neural network","authors":"Sakshi Dhingra, Dharminder Kumar","doi":"10.1080/02522667.2022.2133222","DOIUrl":"https://doi.org/10.1080/02522667.2022.2133222","url":null,"abstract":"Abstract Hyperspectral images usually comprise several continuous spectral bands that represent the category of similar objects or material within the captured scene. These high-dimensional data structures have a high level of correlation and possess unique information that can be used for precise image classification. The precise selection of useful features from these high dimensional band information is very important to reduce the challenge of hyper spectral image classification approaches. Nowadays, metaheuristic algorithms are immensely utilized as a promising tool for hyperspectral image classification. In the present research work, hyperspectral images are classified with the various combinations of meta-heuristic approaches and the neural network including the mostly used Cuckoo Search (CS) optimization algorithm to resolve the global optimization search problems considering the improvement needed in image classification. Further, the strength of CS is improved using the integration of the Genetic Algorithm (GA) fitness function within the CS. The feature selection is performed by the hybrid CS and GA algorithm and the optimized features are then fed to ANN for training and classification. The paper has shown a comparative analysis of various meta heuristics techniques with ANN on parameters like kappa coefficient, Class accuracy and overall Accuracy and the designed algorithms are tested on the Indian Pines dataset. The proposed CS and GA with ANN outperformed the two already existing works with an overall average accuracy of 97.30% and a kappa coefficient of 0.9760.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"43 1","pages":"2167 - 2179"},"PeriodicalIF":1.4,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48383690","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}

引用次数: 0

A novel approach for BOA trained ANN for channel equalization problems 一种基于BOA训练的人工神经网络信道均衡问题的新方法

IF 1.4

JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES Pub Date : 2022-11-17 DOI: 10.1080/02522667.2022.2153996

Badal Acharya, Priyadarsan Parida, R. N. Panda, P. K. Mohapatra

引用次数: 0

Enriched formulas to conjugate gradient method for removing impulse noise images 共轭梯度法去除脉冲噪声图像的丰富公式

IF 1.4

JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES Pub Date : 2022-11-17 DOI: 10.1080/02522667.2022.2122199

Basim A. Hassan, Ali Ahmed A. Abdullah

引用次数: 0

Hybrid deep learning model for Arabic text classification based on mutual information 基于互信息的阿拉伯语文本分类混合深度学习模型

IF 1.4

JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES Pub Date : 2022-11-17 DOI: 10.1080/02522667.2022.2060910

Farah A. Abdulghani, N. A. Abdullah

{"title":"Hybrid deep learning model for Arabic text classification based on mutual information","authors":"Farah A. Abdulghani, N. A. Abdullah","doi":"10.1080/02522667.2022.2060910","DOIUrl":"https://doi.org/10.1080/02522667.2022.2060910","url":null,"abstract":"Abstract Text categorization refers to the process of grouping text or documents into classes or categories according to their content, which is a significant task in natural language processing. The majority of the present work focused on English text, with a few experiments on Arabic text. The text classification process consists of many steps, from preprocessing documents (removing stop words and stem method), to feature extraction and classification phase. A new improved approach for Arabic text categorization was proposed using mutual information in a hybrid deep learning model for classification. To test the proposed model, two datasets of Arabic documents are employed. The experimental results demonstrate that employing the proposed mutual information exceeds other prior techniques in terms of performance. In Akhbarona corpus, the Multi-Layer Perceptron achieved a minimum accuracy of 96.09%, while the hybrid Convolution-Long Short-Term Memory had a performance level of 99.28%. In Khaleej corpus, the Gated Recurrent Unit had the maximum accuracy of 98.23%, while Multi-Layer Perceptron had the lowest accuracy of 97.23%","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"43 1","pages":"1901 - 1908"},"PeriodicalIF":1.4,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42843974","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}

引用次数: 0

A genetic algorithm based decision support system for forecasting security prices in stock index 基于遗传算法的股票指数证券价格预测决策支持系统

IF 1.4

JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES Pub Date : 2022-11-17 DOI: 10.1080/02522667.2022.2133221

V. Kapoor, S. Dey

引用次数: 0

Small object detection using retinanet with hybrid anchor box hyper tuning using interface of Bayesian mathematics 基于贝叶斯数学接口的retinanet混合锚盒超调谐小目标检测

IF 1.4

JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES Pub Date : 2022-11-17 DOI: 10.1080/02522667.2022.2133217

R. Chaturvedi, Udayan Ghose

{"title":"Small object detection using retinanet with hybrid anchor box hyper tuning using interface of Bayesian mathematics","authors":"R. Chaturvedi, Udayan Ghose","doi":"10.1080/02522667.2022.2133217","DOIUrl":"https://doi.org/10.1080/02522667.2022.2133217","url":null,"abstract":"Abstract In recent years object detection system has been improved by many folds due to many novel deep learning models. Deep learning has outperformed the existing traditional computer vision techniques. In recent many deep learning models uses the concept of anchor box, the model proposes various anchor boxes on the images. The models generally use a classification model and a regression models, the regression model is used to predict the position of next possible anchor box and the classification is used to validate the anchor box. The hyper tuning of these models are generally based on the anchor box specifications, many researchers have used an optimized anchor box dimensions which is obtained for a specific dataset, due to which the accuracy increases drastically but the model are not scalable on any other data set. We propose a new hybrid anchor box optimization technique by using a variant of Bayesian optimization and sub sampling for small object detection using retina net model with resnet backbone. Our hybrid model is scalable over various datasets, the model is used on visdrone dataset and the result shows a 3.7% improvement in MAP result.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":"43 1","pages":"2099 - 2110"},"PeriodicalIF":1.4,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46051248","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}

引用次数: 2