Hybrid journal (It can contain Open Access articles) ISSN (Print) 1740-7494 - ISSN (Online) 1740-7508 Published by Inderscience Publishers[448 journals]
Authors:M. Durgadevi, R. Kalpana Pages: 7 - 24 Abstract: Diabetes mellitus is a major health challenge around the world. The blood glucose level is one of the major factors in the human body and a significant increase in its level can cause many harmful effects in human life. It is expected that early diagnosis of diabetes mellitus can lead to rapid and effective treatment of glycemic control. As the number of people who suffer from diabetes mellitus increases significantly, a study on diabetes mellitus prediction was done through well-known methods in data mining (DM). In this paper, a genetic algorithm (GA)-based suppressor mutation (SM) optimisation rule miner has been proposed as a cooperative approach for prediction of diabetes mellitus. A novel fitness function has been incorporated into the GA-SM approach to generate a comprehensive optimal rule set while balancing accuracy, sensitivity and specificity. The proposed rule miner was compared against three rule-based algorithms, namely CN2, J48 and BF tree on the Pima Indians Diabetes Dataset with 768 patient records using ten-fold cross validation. The results obtained prove that the proposed GA-SM approach has outperformed CN2, J48 and BF tree with respect to accuracy and kappa. Keywords: diabetes mellitus; data mining; genetic algorithm; suppressor mutation Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 7 - 24 PubDate: 2020-02-22T23:20:50-05:00 DOI: 10.1504/EG.2020.105251 Issue No:Vol. 16, No. 1/2 (2020)
Authors:S. Sathurthi, K. Saruladha Pages: 25 - 38 Abstract: Diabetes mellitus is one of the prominent health challenges in the world. Diabetes is a dangerous, metabolic disease that caused by human blood sugar level and progresses throughout life. In supervised learning-based systems have been proposed that incorporate ensemble learning techniques for diabetes prediction depends upon the diagnostic measurement of the diabetes patient. In this paper, voting classifier were used for combining the various ensemble and base classifiers for designing diabetes disease prediction. Voting mechanism helps to build the multiple ensemble and base classifier model. The accuracy of ensemble of ensemble classifiers has resulted in high rate of accuracy (79%) when compared to the ensemble of base classifiers (77%) with majority rule voting (MRV) and weighted majority voting (WMV) models. Hence, ensemble of ensemble classifier was chosen as the best model for diabetes healthcare prediction. This system has been experimented with Pima Indian diabetes UCI dataset and its implemented in python language. Keywords: base classifiers; ensemble classifiers; cross validation; bagging; boosting; decision tree; majority rule voting; MRV; weighted majority voting; WMV Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 25 - 38 PubDate: 2020-02-22T23:20:50-05:00 DOI: 10.1504/EG.2020.105250 Issue No:Vol. 16, No. 1/2 (2020)
Authors:G. Anitha, Mohamed Ismail, S.K. Lakshmanaprabu Pages: 39 - 55 Abstract: Over the years, health informatics and eHealth gained more popularity in health care application. The collection of eHealth data becomes easier due to the advancement of digital technology. In this paper, the e-Health based supporting system is developed for the classification of a retinal disease called CNV. CNV is a retinal disease caused due to the growth of abnormal blood vessels in the choroidal layer. A good classifier for CNV data makes the process of identifying the disease easier and it will help the medical practitioners to give the treatment at the right time. A comparison has been done among different machine learning classifiers such as support vector machine (SVM), k-nearest neighbours (kNN), neural network (NN), ensemble and naive Bayes classifiers and they are tested and evaluated based on accuracy and training time. From the results, it is observed that kNN classifier outperforms the other classifiers in all aspects. Keywords: choroidal neovascularisation; optical coherence tomography; OCT; machine learning classifiers; support vector machine; SVM; kNN classifier; naive Bayes classifier Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 39 - 55 PubDate: 2020-02-22T23:20:50-05:00 DOI: 10.1504/EG.2020.105254 Issue No:Vol. 16, No. 1/2 (2020)
Authors:Prasad Koti, P. Dhavachelvan, T. Kalaipriyan, Sariga Arjunan, J. Uthayakumar, Pothula Sujatha Pages: 56 - 68 Abstract: Prediction of kidney disease (KD) gains more importance in the medical decision support systems. As the medical dataset are massive in size, effective techniques are required to produce accurate results. This paper proposes a hybrid harmony search (HM-L) algorithm with Levi distribution to properly predict KD at appropriate time. In this research work, correlation-based feature selection (CFS) is used as a feature selection technique. The effectiveness of hybrid harmony search (HS) algorithm is validated by employing it against a set of dataset. The obtained results of applied datasets without and with feature selection are compared to one another. The experimental results imply that the HM-L algorithm attains significant results than existing methods such as HS algorithm, biogeography optimisation algorithm (BBO), grey wolf optimisation (GWO), AL particle swarm optimisation algorithm (ALPSO) and artificial bee colony (ABC) algorithm. The presented HM-L model attains a sensitivity of 96, specificity of 93.33, accuracy of 95, F-score of 96 and kappa value of 0.89. Keywords: feature selection; harmony search algorithm; intelligent algorithms; Levi distribution Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 56 - 68 PubDate: 2020-02-22T23:20:50-05:00 DOI: 10.1504/EG.2020.105236 Issue No:Vol. 16, No. 1/2 (2020)
Authors:M. Ganesan, N. Sivakumar, M. Thirumaran Pages: 69 - 83 Abstract: In the present days, e-health services offer various decision support systems in healthcare sector. These systems make use of internet of medical things (IoMT) devices and cloud platform to offer services to millions of people. In this paper, we develop an IoT with cloud-based brain tumour detection model using convolution neural network (CNN). Here, the input MRI brain images are captured by the use of medical equipments as well as IoT devices is used to transmit data to the cloud. In the cloud, the D-CNN model can be executed to identify the presence of disease and classify the brain tumour as malignant or benign. The presented D-CNN model is employed to a set of benchmark BRATS 2015 challenge dataset. The presented model attains maximum classifier performance with the sensitivity value of 97.17, specificity of 98.77 and accuracy of 98.07. Keywords: e-health services; brain tumour; cloud computing; convolution neural network; CNN; decision support; internet of medical things; IoMT Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 69 - 83 PubDate: 2020-02-22T23:20:50-05:00 DOI: 10.1504/EG.2020.105240 Issue No:Vol. 16, No. 1/2 (2020)
Authors:P. Nagaraj, P. Deepalakshmi Pages: 84 - 100 Abstract: Recommender systems are emerging up very popular as they are able to predict the preferences of a user. This makes the proposal dependent on the user profile, past evaluations or/and extra learning, for example, user logical information and instructions. It can also be applied to healthcare area by taking advantage of environment information to sustain health improvement and disease prohibition. The utilisation of recommender framework has been efficient in both industry and the intellectual world. Recommender framework has additionally discovered its way into the healthcare discipline interval with shifted applications. The motivation behind this investigation is to comprehend the pattern of recommender framework applications in healthcare services by analysing and to give specialists and research knowledge and future supervision. The proposed work centres on giving a complete outline of the recommender frameworks in e-health service information to patients whenever, wherever and whatever. Health information systems are turning into an essential stage for healthcare services. In this specific circumstance, health recommender system is exhibited as correlative toll in decision-making process in healthcare services and administrations. This paper exhibits current improvements in the market, challenges and opportunities in e-health concerning health recommender system and developing methodologies. Keywords: recommender system; health recommender system; e-healthcare; healthcare integrated information systems Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 84 - 100 PubDate: 2020-02-22T23:20:50-05:00 DOI: 10.1504/EG.2020.105256 Issue No:Vol. 16, No. 1/2 (2020)
Authors:Maganti Venkatesh, S. Sathyalakshmi Pages: 101 - 117 Abstract: Many of e-learning systems in their web-based courses do not have personalisation based on individual needs and their capabilities. Main challenging aspect of personalised delivery of e-learning is concerned with an adaptive course delivery along with content delivery. Personalised e-learning environment provide recommendations to learning community for supporting and also helping them go through the process of e-learning, as it plays a crucial role in promotion of smart learning in smart cities. In this work, a novel framework namely, personalised bee recommender for e-learning (PBReL) based on artificial bee colony (ABC) optimisation is proposed to build a structure of recommendation by using K-means clustering. Many other recommender system are available that made use of ABC to identify its optimal learning path. Experiments are carried out by using web links and contents of Moodle-based learning management system (LMS). Results show that the proposed framework obtains higher precision and coverage. Keywords: e-learning; personalisation; smart cities; smart learning; personalised bee recommender for e-learning; PBReL; K-means clustering; artificial bee colony; ABC Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 101 - 117 PubDate: 2020-02-22T23:20:50-05:00 DOI: 10.1504/EG.2020.105253 Issue No:Vol. 16, No. 1/2 (2020)
Authors:Akula V.S. Siva Rama Rao, P. Ranjana Pages: 118 - 136 Abstract: Many government schemes were unsuccessful because lack of proper feedback on the ongoing schemes, where billion dollars investment is going to be in vain. Sentiment analysis is one of best approach to analyse opinions of the peoples on various government schemes. Sentiment analysis and machine learning techniques emerged to analyse huge social media corpora to track people's views on government policies, products and services. Sentiment analysis process consists of various phases which include data discovery, data collection, data pre-processing, and data analysis. Stemming is a process to generate the morphemes in natural language sentences for various applications such as sentiment analysis, information retrieval, and domain analysis. The stemming process involved two major errors, which are over-stemming and under-stemming errors. Most of sentiment analysis natural languages processing applications used Lancaster and Porter stemming algorithms where more than one word inflected into same morpheme, which causes the etymology behaviour of the stemming word and prone to classify the tweets false positives and false negative. The proposed un-prejudice light stemming algorithm prevent etymology behaviour of morpheme and sustain its meaning during stemming process by selecting a word which has maximum number of synonyms in lexical database. Keywords: empower government; government schemes; NLP; social media networks; under-stemming; over-stemming; stemmer weight; sentiment analysis Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 118 - 136 PubDate: 2020-02-22T23:20:50-05:00 DOI: 10.1504/EG.2020.105252 Issue No:Vol. 16, No. 1/2 (2020)
Authors:S. Deva Arul, Meenakshisundaram Iyapparaja Pages: 137 - 154 Abstract: The rapid development of technologies in today's world has become interesting that made millions of people to utilise the major advantages in it. Two main technologies that were emerging in modern society are big data and the social internet of things. Several researchers have studied and developed a major concept of using big data with SIoT and the security development of maintain a large amount of data. In this paper, deep survey regarding the concepts behind the big data analytics with the social internet of things (SIoT) was studied and analysed. Furthermore, the machine learning techniques that were used in previous works were analysed and comparisons of various methods are discussed. The performance comparison of various classifiers on different datasets is shown and SVM has more than 90% of accuracy when compared with other algorithms. KNN has 64% of accuracy which is lowest of any classifier than NB and NN. Keywords: big data; social internet of things; SIoT; frequent itemset mining; FIM; machine learning Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 137 - 154 PubDate: 2020-02-22T23:20:50-05:00 DOI: 10.1504/EG.2020.105238 Issue No:Vol. 16, No. 1/2 (2020)
Authors:S. Deva Arul, Meenakshisundaram Iyapparaja Pages: 155 - 169 Abstract: A smart city is a modern habitat that uses internet of things (IOT) to collect data and manage resources to provide high level of services to people. Information and communication technology (ICT) is a technique to enhance the quality of a smart city. Semantic web and cloud servers are the resources to provide data for smart city management tools. Riyadh is one of the smart cities in Kingdom of Saudi Arabia. Dust storm and floods are the most common hazards in the city. Existing smart city management could not provide an effective solution for the natural hazards. There is a necessity for smart city applications to optimise data and provide an optimum accuracy in output. The objective of the paper is to provide a solution for natural hazards and provide effective management of Riyadh city. A machine learning technique, interior search algorithm is used in the proposed study. It is used in the research for the extraction of knowledge from complex data. The efficiency of proposed method is compared with state of the art algorithms. The proposed method has achieved an accuracy of 87% in the management of natural hazards. Keywords: smart city; internet of things; IOT; machine learning; artificial intelligence; search techniques; information and communication technology; ICT; Kingdom of Saudi Arabia Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 155 - 169 PubDate: 2020-02-22T23:20:50-05:00 DOI: 10.1504/EG.2020.105260 Issue No:Vol. 16, No. 1/2 (2020)
Authors:R. Rajakumar, D. Sivanandakumar, J. Uthayakumar, T. Vengattaraman, K. Dinesh Pages: 170 - 189 Abstract: System lifetime is the crucial problem of wireless sensor networks (WSNs), and exploiting environmental energy provides a potential solution. Boost convertor can be employed in WSN to achieve energy efficiency. In order to achieve better performance, PID controller is combined with boost convertor. However, tuning the PID controller is crucial task whenever the input voltage fluctuates. In this work, a novel algorithm namely accelerated grey wolf optimisation (AGWO) is proposed to improve the convergence speed and to eradicate the local optima stagnation. AGWO algorithm utilises a balanced intensification and diversification techniques to eradicate the local optima struck. The observed results conveys that AGWO achieves minimum percentage overshoot (9%), settling time (0.894), rise time (0.50) and peak time (0.57) which is better compared to other comparative algorithms. Additionally, it has been observed that AGWO is able to achieve comparatively better success performance in a complex environment. Keywords: wireless sensor network; WSN; smart sensors; PID controller; grey wolf optimisation; GWO; boost convertor problem Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 170 - 189 PubDate: 2020-02-22T23:20:50-05:00 DOI: 10.1504/EG.2020.105237 Issue No:Vol. 16, No. 1/2 (2020)
Authors:R. Buvanesvari, A. Rijuvana Begum Pages: 190 - 209 Abstract: Wireless sensor networks involve a massive number of sensor nodes often deployed to observe the physical world. Energy efficiency is the challenging issue in the design of WSN. For attaining energy efficient characteristic, clustering techniques has been employed. But, it suffers from hot spot issue which defines that the cluster heads (CHs) closet to base station are burdened with more traffic compared to CHs located far away from BS due to multihop communication. To resolving this, we present a social spider-based unequal clustering protocol (SSUCP) for WSN. SSUCP is based on the nature of social spiders to select the proper CHs and cluster size. Based on the fitness functions and node parameters, the interested decision of selecting the proper CHs and cluster size were made. The SSUCP is implemented in MATLAB and an extensive experimentation takes place under three situations based on distance to BS for ensuring the consistent results of the proposed method. In addition, the SSUCP is validated in terms of energy efficiency and network lifetime analysis. The attained experimental outcome verified that the SSUCP is the superior one over the compared methods. Keywords: smart cities; WSN; social spider; hot spot issue; clustering Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 190 - 209 PubDate: 2020-02-22T23:20:50-05:00 DOI: 10.1504/EG.2020.105235 Issue No:Vol. 16, No. 1/2 (2020)
Authors:V. Kavitha, G. Balakrishnan Pages: 210 - 221 Abstract: Stereo vision is a subfield of computer vision that tends to an essential research issue of reproducing the three-dimensional directions and focuses for depth estimation. This paper gives a relative investigation of stereo vision and matching techniques, utilised to resolve the correspondence problem. The investigation of matching algorithms is done by the use of extensive experiments on the Middlebury benchmark dataset. The tests concentrated on an examination of three stereovision techniques namely mean shift algorithm (MSA), seed growing algorithm (SGA) and multi-curve fitting (MCF) algorithm. With a specific end goal to evaluate the execution, some statistics related insights were computed. The experimental results demonstrated that best outcome is attained by the MCF algorithm in terms of depth estimation, disparity estimation and CT. The presented MCF algorithm attains a minimum computation time (CT) of 2 s whereas the other MSA and SGA require a maximum CT of 8.9 s and 7 s, respectively. The simulation results verified that the MCF algorithm reduces the processing time in a significant way than the compared methods. Keywords: stereo matching; stereo vision; multi-fitting; Middlebury; smart cities Citation: Electronic Government, an International Journal, Vol. 16, No. 1/2 (2020) pp. 210 - 221 PubDate: 2020-02-22T23:20:50-05:00 DOI: 10.1504/EG.2020.105245 Issue No:Vol. 16, No. 1/2 (2020)
Hybrid journal (It can contain Open Access articles)
[448 journals]
