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Hybrid mechanism for medical data classification 医疗数据分类的混合机制
Int. J. Adv. Intell. Paradigms Pub Date : 2021-07-09 DOI: 10.1504/ijaip.2021.10039496
A. Tikotikar, M. Kodabagi
{"title":"Hybrid mechanism for medical data classification","authors":"A. Tikotikar, M. Kodabagi","doi":"10.1504/ijaip.2021.10039496","DOIUrl":"https://doi.org/10.1504/ijaip.2021.10039496","url":null,"abstract":"In recent years, medical data mining is the hot topic of research. Earlier extraction was done manually so the process was time consuming. Thus, to solve this drawback machine learning technique was implemented to extract knowledge from the large dataset. In this paper, the medical data processed for removal of missing values, then orthogonal local preserving projection (OLPP) algorithm is employed for feature dimension reduction. Then, fuzzy rules are generated and optimised by binary cuckoo search algorithm. Further, the classification is carried out using the generated fuzzy rules and the best rule is selected based on the accuracy using the decision tree classifier. The performance of the proposed technique has been evaluated using University of California Irvine (UCI) datasets namely Cleveland, Hungarian, Mammographic masses, PID and Switzerland. After observation and comparison, the method achieves highest accuracy of 97.3% for PID dataset whereas the existing method provides 89.5% accuracy for PID dataset.","PeriodicalId":151740,"journal":{"name":"Int. J. Adv. Intell. Paradigms","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133627217","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
Improving the accuracy of item recommendations by combining collaborative and content-based recommendations: a hybrid approach 通过结合协作和基于内容的推荐来提高项目推荐的准确性:一种混合方法
Int. J. Adv. Intell. Paradigms Pub Date : 2021-07-09 DOI: 10.1504/ijaip.2021.10039495
Desabandhu Parasuraman, Sathiyamoorthy Elumalai
{"title":"Improving the accuracy of item recommendations by combining collaborative and content-based recommendations: a hybrid approach","authors":"Desabandhu Parasuraman, Sathiyamoorthy Elumalai","doi":"10.1504/ijaip.2021.10039495","DOIUrl":"https://doi.org/10.1504/ijaip.2021.10039495","url":null,"abstract":"Recommender systems facilitate the users by providing the ample information of the items or the products they are interested. Users would not be aware of item details without the help of recommender systems due to the size of information available on the web. Collaborative filtering and content-based filtering are the two traditional filtering techniques of recommender systems. Both the filtering techniques have their advantages and certainly the disadvantages too. This can be solved by combining both the filtering techniques and improves the accuracy of recommendations. This leads to system as a hybrid recommender system. This paper presents a novel hybrid approach by combining a dynamic item-based collaborative filtering with the content-based filtering. Time variance and machine learning algorithms are applied on the filtering techniques to overcome the problems in recommendations. The approach is demonstrated using the MovieLens data sets to ensure the effectiveness of the proposed hybrid system.","PeriodicalId":151740,"journal":{"name":"Int. J. Adv. Intell. Paradigms","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124891448","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
Evolutionary cross-layer interactive model for sensor communication 传感器通信的进化跨层交互模型
Int. J. Adv. Intell. Paradigms Pub Date : 2021-07-09 DOI: 10.1504/ijaip.2021.10039493
Shoba Chandra, K. Patil, S. Talanki
{"title":"Evolutionary cross-layer interactive model for sensor communication","authors":"Shoba Chandra, K. Patil, S. Talanki","doi":"10.1504/ijaip.2021.10039493","DOIUrl":"https://doi.org/10.1504/ijaip.2021.10039493","url":null,"abstract":"Cross-layer based approach has significant benefits compared to conventional layered based routing approach in wireless sensor network (WSN); however, existing approaches in this direction have been found to offer a symptomatic solution towards any problem. The proposed system emphasises on establishing interactivity among the logical link layer, MAC layer, and network layer. Usage of CLIF model ensures message ranking mechanism that assists in tagging the priorities to the control message which facilitates to offer better signal processing services by WSN. Hence, an evolutionary novel approach is introduced in this paper which predominantly emphasise on the interaction between the cross-layer. The model is developed to 1) ensure better network lifetime, 2) incorporate better supportability to ad-hoc routing policies, 3) leverage cooperative transmission scheme, and 4) maintain sustainability between data forwarding mechanism and energy consumption. The outcome of the simulation result shows that the proposed scheme offers minimal delay, maximised data delivery, and reduced energy consumption in WSN.","PeriodicalId":151740,"journal":{"name":"Int. J. Adv. Intell. Paradigms","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130469604","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
Disabled person emotion recognition in EEG signal using deep neural network 基于深度神经网络的脑电信号残疾人情绪识别
Int. J. Adv. Intell. Paradigms Pub Date : 2021-07-09 DOI: 10.1504/ijaip.2021.10039494
M. Kumar, Krishnappa Suresh
{"title":"Disabled person emotion recognition in EEG signal using deep neural network","authors":"M. Kumar, Krishnappa Suresh","doi":"10.1504/ijaip.2021.10039494","DOIUrl":"https://doi.org/10.1504/ijaip.2021.10039494","url":null,"abstract":"Emotion recognition is an important field of research in brain-computer interactions. As technology and the understanding of emotions are advancing, there are growing opportunities for automatic emotion recognition systems. The brain uses the neuromuscular channels to communicate and control its external environment, however many disorders can disrupt these channels. An electroencephalogram (EEG) signals are generated in the human brain, communicate with several neurons and low amplitude signals. In this paper, the hybrid feature extraction (Renyi and differential entropy) was performed on the acquired EEG signal in order to achieve feature subsets. The respective feature values were given as the input for a multi-objective classifier: deep neural network (DNN) for classifying the disabled persons and their emotions. The proposed technique improves the emotion prediction accuracy for different sessions. The emotional recognition classification model includes three states: positive, neutral and negative. In experimental analysis, the proposed approach classifies the disabled persons and their emotions by means of specificity, sensitivity, and accuracy. The experimental outcome shows that the proposed methodology improved accuracy in emotion classification up to 8.01% compared to the existing methods: k-nearest neighbours (KNN) and support vector machine (SVM).","PeriodicalId":151740,"journal":{"name":"Int. J. Adv. Intell. Paradigms","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121868626","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
MRI brain segmentation using correlation based on adaptively regularised kernel-based fuzzy C-means clustering 基于自适应正则化核模糊c均值聚类的MRI脑分割
Int. J. Adv. Intell. Paradigms Pub Date : 2021-05-14 DOI: 10.1504/IJAIP.2021.115250
Y. Ambica, N. S. Chandra
{"title":"MRI brain segmentation using correlation based on adaptively regularised kernel-based fuzzy C-means clustering","authors":"Y. Ambica, N. S. Chandra","doi":"10.1504/IJAIP.2021.115250","DOIUrl":"https://doi.org/10.1504/IJAIP.2021.115250","url":null,"abstract":"Brain tumour detection is the most challenging aspect in the field of medical image processing and analysis. Whereas, the traditional techniques used in MRI brain tumour segmentation are extremely time consuming tasks. In this paper, the proposed approach contains two major steps: image acquisition and segmentation. Initially, the brain tumour detection was assessed by employing T1-weighted contrast enhanced magnetic resonance imaging (T1-WCEMRI) database. After image acquisition, segmentation was carried-out by using correlation based adaptively regularised kernel-based fuzzy C-means (ARKFCM) clustering along with Otsu thresholding. In conventional clustering methodologies, it was very hard to detect the ill-defined masses that highly decrease the segmentation accuracy. To address this concern, the kernel function in ARKFCM was replaced by a correlation function for localising the object in a complex template. In experimental analysis, the proposed approach distinguishes the normal brain region and brain tumour region by means of dice coefficient, Jaccard coefficient, true positive rate (TPR), false positive rate (FPR) and accuracy. The experimental outcome shows that the proposed methodology delivered average accuracy of 99.213% in brain tumour detection. The proposed methodology improved accuracy in brain tumour segmentation up to 3-3.2% compared to the existing methods: FCM and ARKFCM.","PeriodicalId":151740,"journal":{"name":"Int. J. Adv. Intell. Paradigms","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120914773","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
Determination of reliability index of cantilever retaining wall by RVM, MPMR and MARS 用RVM、MPMR和MARS确定悬臂挡土墙可靠度指标
Int. J. Adv. Intell. Paradigms Pub Date : 2021-02-16 DOI: 10.1504/IJAIP.2021.113325
Rahul Kumar, P. Samui, Sunita Kumari, Sanjiban Sekhar Roy
{"title":"Determination of reliability index of cantilever retaining wall by RVM, MPMR and MARS","authors":"Rahul Kumar, P. Samui, Sunita Kumari, Sanjiban Sekhar Roy","doi":"10.1504/IJAIP.2021.113325","DOIUrl":"https://doi.org/10.1504/IJAIP.2021.113325","url":null,"abstract":"Overturning criterion is an important parameter for designing cantilever retaining wall. This study adopts relevance vector machine (RVM) based first order second method (FOSM), minimax probability machine regression (MPMR) based FOSM and multivariate adaptive regression spline (MARS) based FOSM for determination of reliability index of cantilever retaining wall based on overturning criterion. RVM, MPMR and MARS have been used to overcome the limitations of the FOSM method. An example is illustrated how the proposed RVM based FOSM, MPMR based FOSM and MARS based FOSM analysis can be carried out. A comparative study has been carried out between the developed techniques. The results demonstrate that the developed techniques have ability to overcome the limitations of FOSM. This article describes new methods based on RVM, MPMR and MARS for determination of reliability index of retaining wall. The adopted methods eliminate the limitation of FOSM.","PeriodicalId":151740,"journal":{"name":"Int. J. Adv. Intell. Paradigms","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131555087","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
Image compression based on adaptive image thresholding by maximising Shannon or fuzzy entropy using teaching learning based optimisation 基于自适应图像阈值的图像压缩，通过最大化香农或模糊熵，使用基于教学学习的优化
Int. J. Adv. Intell. Paradigms Pub Date : 2021-01-25 DOI: 10.1504/IJAIP.2021.112904
K. Chiranjeevi, U. Jena, M. Rao
{"title":"Image compression based on adaptive image thresholding by maximising Shannon or fuzzy entropy using teaching learning based optimisation","authors":"K. Chiranjeevi, U. Jena, M. Rao","doi":"10.1504/IJAIP.2021.112904","DOIUrl":"https://doi.org/10.1504/IJAIP.2021.112904","url":null,"abstract":"In this paper, teaching leaning based optimisation (TLBO) is used for maximising Shannon entropy or fuzzy entropy for effective image thresholding which leads to better image compression with higher peak signal to noise ratio (PSNR). The conventional multilevel thresholding methods are efficient when bi-level thresholding. However, they are computationally expensive extending to multilevel thresholding since they exhaustively search the optimal thresholds to optimise the objective functions. To overcome this drawback, a TLBO based multilevel image thresholding is proposed by maximising Shannon entropy or fuzzy entropy and results are compared with differential evolution, particle swarm optimisation and bat algorithm and proved better in standard deviation, PSNR, weighted PSNR and reconstructed image quality. The performance of the proposed algorithm is found better with fuzzy entropy compared to Shannon entropy.","PeriodicalId":151740,"journal":{"name":"Int. J. Adv. Intell. Paradigms","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127119881","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
An efficient and optimised approach for secured file sharing in cloud computing 云计算中安全文件共享的有效和优化方法
Int. J. Adv. Intell. Paradigms Pub Date : 2021-01-25 DOI: 10.1504/IJAIP.2021.112905
Neha Agarwal, A. Rana, J. Pandey
{"title":"An efficient and optimised approach for secured file sharing in cloud computing","authors":"Neha Agarwal, A. Rana, J. Pandey","doi":"10.1504/IJAIP.2021.112905","DOIUrl":"https://doi.org/10.1504/IJAIP.2021.112905","url":null,"abstract":"Cloud computing has brought a new revolution to IT industry. In this era, information sharing is more convenient and fast, but applying security mechanism on information while outsourcing it on cloud is challenging. In this paper, we proposed hybrid approach comprising of symmetric and asymmetric algorithm. For encrypting file, we generated optimal and efficient symmetric key using genetic algorithm (GA), further an efficient and secure re-encryption concept has been proposed for ensuring the security of data in unreliable cloud environment. Our results have proved that our proposed approach is more efficient and secured while sharing file with authorised users using cloud in comparison to existing approaches.","PeriodicalId":151740,"journal":{"name":"Int. J. Adv. Intell. Paradigms","volume":"54 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120916809","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
On the convergence and optimality of the firefly algorithm for opportunistic spectrum access 机会频谱接入萤火虫算法的收敛性和最优性
Int. J. Adv. Intell. Paradigms Pub Date : 2021-01-25 DOI: 10.1504/IJAIP.2021.112900
L. Kalabarige, S. Rodda, Shanti Chilukuri
{"title":"On the convergence and optimality of the firefly algorithm for opportunistic spectrum access","authors":"L. Kalabarige, S. Rodda, Shanti Chilukuri","doi":"10.1504/IJAIP.2021.112900","DOIUrl":"https://doi.org/10.1504/IJAIP.2021.112900","url":null,"abstract":"Meta-heuristic algorithms have been proven to be efficient for engineering optimisation. However, the convergence and accuracy of such algorithms depends on the objective function and also on several choices made during algorithm design. In this paper, we focus on the firefly algorithm for optimal channel allocation in cognitive radio networks. We study the effect of various probability distributions including the Levy alpha stable distribution for randomisation of firefly movement. We also explore various functions for converting firefly positions from the continuous space to the discrete space, as is necessary in the spectrum allocation problem. Simulation results show that in most cases, Levy flight gives better convergence time and results for common optimisation problems such as maximising the overall channel utilisation, maximising the channel allocation for the bottleneck user and maximising proportional fairness. We also note that no single discretisation function gives both good convergence and optimality.","PeriodicalId":151740,"journal":{"name":"Int. J. Adv. Intell. Paradigms","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114713477","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
Flower pollination-based K-means algorithm for medical image compression 基于花授粉的医学图像压缩K-means算法
Int. J. Adv. Intell. Paradigms Pub Date : 2021-01-25 DOI: 10.1504/IJAIP.2021.10035157
G. Kumari, G. Rao, B. Rao
{"title":"Flower pollination-based K-means algorithm for medical image compression","authors":"G. Kumari, G. Rao, B. Rao","doi":"10.1504/IJAIP.2021.10035157","DOIUrl":"https://doi.org/10.1504/IJAIP.2021.10035157","url":null,"abstract":"Image compression plays a significant role in digital image storage and transmission because of limited availability of storage devices space and insufficient bandwidth and is beneficial for all multimedia applications. Magnetic resonance imaging (MRI) of a human body produces an image of huge size and is to be compressed but medical field demands high image quality for better diagnosis of disease. In this technologically advanced world, intelligence systems try to simulate human intelligence. It is applied in the field of engineering, industry, medicine and education problems and it makes decisions by using the several inputs. However, the search process is enormous and convergence time depends on algorithm structure. In this paper first time methaheuristic algorithms are used for near optimum solutions. This paper introduces flower pollination algorithm (FPA)-based vector quantisation for better image compression with better reconstructed image quality. Performance of proposed method is evaluated by using peak signal to noise ratio (PSNR), mean square error (MSE) and fitness function.","PeriodicalId":151740,"journal":{"name":"Int. J. Adv. Intell. Paradigms","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132609382","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: 13
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