International Journal of Intelligent Information and Database Systems最新文献_第9页

Development of hybrid model for improving the prediction of dengue-human protein interaction for anti-viral drug discovery 开发用于改进登革热-人蛋白相互作用预测的杂交模型，用于抗病毒药物的发现

International Journal of Intelligent Information and Database Systems Pub Date : 2020-09-09 DOI: 10.1504/ijiids.2020.10031590

R. Revathy, A. Fathima, S. Balamurali, G. Murugaboopathi

{"title":"Development of hybrid model for improving the prediction of dengue-human protein interaction for anti-viral drug discovery","authors":"R. Revathy, A. Fathima, S. Balamurali, G. Murugaboopathi","doi":"10.1504/ijiids.2020.10031590","DOIUrl":"https://doi.org/10.1504/ijiids.2020.10031590","url":null,"abstract":"Dengue fever is the most common viral disease caused by mosquitoes. Due to the lack of curable drugs, there is an urgent need to develop anti-viral against dengue disease. Several innovative computational approaches were incorporated for the discovery of a new lead molecule that acts on the dengue virus target. The target can be a viral or host protein. Predicting the type of interaction between the virus and human protein will give better knowledge in developing therapeutics against the dengue disease. The main objective of this study is to propose a hybrid model which combines feed forward back propagation neural network (FFBPNN) with firefly algorithm to predict the dengue-human protein interaction. The novelty in this study is to focus on optimising the weights and bias of the artificial neural network to improve the efficiency of algorithm. While comparing with existing C4.5 and FFBPNN classification algorithms, the results show that the proposed hybrid method fitted the interaction data efficiently and predicts the interaction type which leads to the development of anti-viral drugs. The accuracy of the classification gained by C4.5 is 88%, FFBPNN is 97% and hybrid FFBPNN is 99%.","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"20 1","pages":"479-490"},"PeriodicalIF":0.0,"publicationDate":"2020-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87008657","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

Optimal bag-of-features using random salp swarm algorithm for histopathological image analysis 基于随机salp群算法的最优特征袋算法用于组织病理图像分析

International Journal of Intelligent Information and Database Systems Pub Date : 2020-08-28 DOI: 10.1504/ijiids.2020.10031678

V. Rachapudi, G. L. Devi

引用次数: 5

A new software development paradigm for intelligent information systems 智能信息系统软件开发新范式

International Journal of Intelligent Information and Database Systems Pub Date : 2020-08-26 DOI: 10.1504/ijiids.2020.10031608

Pooja Dehraj, Arun Sharma

{"title":"A new software development paradigm for intelligent information systems","authors":"Pooja Dehraj, Arun Sharma","doi":"10.1504/ijiids.2020.10031608","DOIUrl":"https://doi.org/10.1504/ijiids.2020.10031608","url":null,"abstract":"The continuous growth in software management cost requires the development of self-managed software systems. Using self-managed property, a system will take intelligent decisions to make a system work properly. Autonomic computing is the technique, which is used to develop such systems. Autonomic computing systems are highly reliable software systems. To enhance the quality of software systems, implementation of autonomic computing-based software development life cycle process may be a novel idea. It involves autonomous decision making by the autonomic component during the development of software. This approach reduces the complexity of the software development process. In addition, it resolves the purpose of autonomic computing to reduce software complexity and do real-time exception handling. In this paper, the implementation of the autonomic advisor-based software development process is proposed using the cloud computing technique. Cloud computing helps the developers to develop software, applications using deliverable services such as platform, infrastructure, and software. During the implementation and usage of autonomic advisor, the database becomes heavier. Therefore, to resolve such issues, cloud computing will be a beneficiary step. Other benefits of such an autonomous software development life cycle process are discussed further in this paper.","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"76 1","pages":"356-375"},"PeriodicalIF":0.0,"publicationDate":"2020-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77815551","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

A novel DeepCNN model for denoising analysis of MRI brain tumour images 一种新的用于MRI脑肿瘤图像去噪分析的DeepCNN模型

International Journal of Intelligent Information and Database Systems Pub Date : 2020-08-26 DOI: 10.1504/ijiids.2020.10031611

B. Srinivas, G. Rao

引用次数: 1

An Earth mover's distance-based undersampling approach for handling class-imbalanced data 一种用于处理类不平衡数据的基于距离的欠采样方法

International Journal of Intelligent Information and Database Systems Pub Date : 2020-08-26 DOI: 10.1504/ijiids.2020.10031612

G. Rekha, V. Reddy, A. Tyagi

{"title":"An Earth mover's distance-based undersampling approach for handling class-imbalanced data","authors":"G. Rekha, V. Reddy, A. Tyagi","doi":"10.1504/ijiids.2020.10031612","DOIUrl":"https://doi.org/10.1504/ijiids.2020.10031612","url":null,"abstract":"Imbalanced datasets typically make prediction accuracy difficult. Most of the real-world data are imbalanced in nature. The traditional classifiers assume a well-balanced class distribution for training data but in practical datasets show up an imbalance, thus obscure a classifier and degrade its capability to learn from such imbalanced datasets. Data pre-processing approaches address this concern by using either random undersampling or oversampling techniques. In this paper, we introduce Earth mover's distance (EMD), as a similarity measure, to find the samples similar in nature and eliminate them as redundant from the dataset. Earth mover's distance has received a lot of attention in wide areas such as computer vision, image retrieval, machine learning, etc. The Earth mover's distance-based undersampling approach provides a solution at the data level to eliminate the redundant instances in majority samples without any loss of valuable information. This method is implemented with five conventional classifiers and one ensemble technique respectively, like C4.5 decision tree (DT), k-nearest neighbour (k-NN), multilayer perceptron (MLP), support vector machine (SVM), naive Bayes (NB) and AdaBoost technique. The proposed method yields a superior performance on 21 datasets from Keel repository.","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"15 1","pages":"376-392"},"PeriodicalIF":0.0,"publicationDate":"2020-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85901048","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: 4

Machine learning-based book recommender system: a survey and new perspectives 基于机器学习的图书推荐系统:综述与新视角

International Journal of Intelligent Information and Database Systems Pub Date : 2020-08-25 DOI: 10.1504/ijiids.2020.10031604

Khalid Anwar, Jamshed Siddiqui, S. S. Sohail

{"title":"Machine learning-based book recommender system: a survey and new perspectives","authors":"Khalid Anwar, Jamshed Siddiqui, S. S. Sohail","doi":"10.1504/ijiids.2020.10031604","DOIUrl":"https://doi.org/10.1504/ijiids.2020.10031604","url":null,"abstract":"The exponential growth of recommender systems research has drawn the attention of the scientific community recently. These systems are very useful in reducing information overload and providing users with the items of their need. The major areas where recommender systems have contributed significantly include e-commerce, online auction, and books and conference recommendation for academia and industrialists. Book recommender systems suggest books of interest to users according to their preferences and requirements. In this article, we have surveyed machine learning techniques which have been used in book recommender systems. Moreover, evaluation metrics applied to evaluate recommendation techniques is also studied. Six categories for book recommendation techniques have been identified and discussed which would enable the scientific community to lay a foundation of research in the concerned field. We have also proposed future perspectives to improve recommender system. We hope that researchers exploring recommendation technology in general and book recommendation in particular will be finding this work highly beneficial.","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"9 1","pages":"231-248"},"PeriodicalIF":0.0,"publicationDate":"2020-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88954754","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: 15

A new weighted two-dimensional vector quantisation encoding method in bag-of-features for histopathological image classification 一种新的特征袋加权二维矢量量化编码方法用于组织病理图像分类

International Journal of Intelligent Information and Database Systems Pub Date : 2020-08-25 DOI: 10.1504/ijiids.2020.10031594

Raju Pal, M. Saraswat

{"title":"A new weighted two-dimensional vector quantisation encoding method in bag-of-features for histopathological image classification","authors":"Raju Pal, M. Saraswat","doi":"10.1504/ijiids.2020.10031594","DOIUrl":"https://doi.org/10.1504/ijiids.2020.10031594","url":null,"abstract":"Automated histopathological image analysis is a challenging problem due to the complex morphological structure of histopathology images. Bag-of-features is one of the prominent image representation methods which has been successfully applied in histopathological image analysis. There are four phases in the bag-of-features method, namely feature extraction, codebook construction, feature encoding, and classification. Out of which feature encoding is one of the prime phases. In feature encoding phase, images are represented in terms of visual words before feeding into support vector machine classifier. However, the feature encoding phase of the bag-of-features framework considers the one feature to encode each image in terms of visual words due to which the system can not use the merits of other features. Therefore, to improve the efficacy of the bag-of-features framework, a new weighted two-dimensional vector quantisation encoding method is proposed in this work. The proposed method is tested on two histopathological image datasets for classification. The experimental results show that the combination of SIFT and ORB features with two dimensional vector quantisation encoding method returns 80.13% and 77.13% accuracy on ADL and Blue Histology datasets respectively which is better than other considered encoding methods.","PeriodicalId":39658,"journal":{"name":"International Journal of Intelligent Information and Database Systems","volume":"16 1","pages":"150-171"},"PeriodicalIF":0.0,"publicationDate":"2020-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87291856","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: 6

Security, privacy and trust: privacy preserving model for internet of things 安全、隐私和信任:物联网的隐私保护模型

International Journal of Intelligent Information and Database Systems Pub Date : 2020-08-25 DOI: 10.1504/ijiids.2020.10031591

S. K. Jain, N. Kesswani, Basant Agarwal

引用次数: 5

Hierarchical clustering on metric lattice 度量格上的层次聚类

International Journal of Intelligent Information and Database Systems Pub Date : 2020-06-26 DOI: 10.1504/ijiids.2020.10030210

Xiangyan Meng, Muyan Liu, Jingyi Wu, Huiqiu Zhou, F. Xu, Qiufeng Wu

引用次数: 1

Quality materialised view selection using quantum inspired artificial bee colony optimisation 使用量子启发的人工蜂群优化的质量物化视图选择

International Journal of Intelligent Information and Database Systems Pub Date : 2020-06-26 DOI: 10.1504/ijiids.2020.10030204

B. Arun

引用次数: 0