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Meta-heuristic algorithms to improve fuzzy C-means and K-means clustering for location allocation of telecenters under e-governance in developing nations
Saibal K. Pal, Rajan Gupta, Sunil K. Muttoo, 2019, original scientific article

Abstract: The telecenter, popularly known as the rural kiosk or common service center, is an important building block for the improvement of e-governance in developing nations as they help in better citizen engagement. Setting up of these centers at appropriate locations is a challenging task; inappropriate locations can lead to a huge loss to the government and allied stakeholders. This study proposes the use of various meta-heuristic algorithms (particle swarm optimization, bat algorithm, and ant colony optimization) for the improvement of traditional clustering approaches (K-means and fuzzy C-means) used in the facility location allocation problem and maps them for the betterment of telecenter location allocation. A dataset from the Indian region was considered for the purpose of this experiment. The performance of the algorithms when applied to traditional facility location allocation problems such as set-cover, P-median, and the P-center problem was investigated, and it was found that their efficiency improved by 20%–25% over that of existing algorithms.
Found in: ključnih besedah
Keywords: ant colony optimization, bat algorithm, common service center, e-governance, fuzzy clustering, meta-heuristic algorithm, particle swarm optimization
Published: 01.04.2021; Views: 763; Downloads: 3
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Binary division fuzzy C-means clustering and particle swarm optimization based efficient intrusion detection for e-governance systems
Sunil K. Muttoo, Saibal K. Pal, 2016, original scientific article

Abstract: With the rapid rise of technology, many unusual and unwanted patterns have been observed in the communication network andrespective systems. This may be attributed to the increase of external threats that cause many security concerns. Such anomalies and unusual behavior lead to a strong need of studying and designing the Intrusion Detection Systems and Clustering. Currently,a variety of clustering methods and their combinations are used to develop an efficient intrusion detection system, but some metrics like low detection rate and high false alarm rate make these models unsatisfactory. The problem of local minima for clustering technique makes their search ability less efficient. An evolutionary technique called particle swarm optimization algorithm, that is based on swarm intelligence, shows a high global maxima search capability. In this paper, these two techniques have been combined to present a novel approach called fuzzy based particle swarm algorithm for the implementation of intrusion detection system. The experiment was conducted on a new data set called Kyoto data set with more number of anomalies. The obtained results were compared with two traditional clustering techniques based on K-Means and Fuzzy C-Means. It was observed that the proposed algorithm outperformed the other two traditional methods on the basis of the Detection Rate and False Alarm rate. In past some researchers have presented the combination of Fuzzy Based Particle Swarm Optimization algorithm to improve the intrusion detection rate,but this rate has been further improved because the algorithm performance depends on the termination condition and the fitness function value which are new in the proposed algorithm. Moreover, cluster numbers have been considered differently in the past, whereas the proposed algorithm works only on binary clustering.
Found in: ključnih besedah
Keywords: intrusion detection, fuzzy C-means clustering, particle swarm optimization, detection rate, e-governance
Published: 01.04.2021; Views: 726; Downloads: 0
.pdf Fulltext (785,04 KB)

BAT algorithm for improving fuzzy C-means clustering for location allocation of rural kiosks in developing countries under e-governance
Saibal K. Pal, Rajan Gupta, Sunil K. Muttoo, 2016, original scientific article

Abstract: Rural Kiosks are important infrastructural pillar in rural regions for internet and basic technology facility all around the world. They are also known as Tele-centers or Common Service Centers and are majorly used by government to promote Electronic Governance. The major characteristic of setting up of Rural Kiosk is their appropriate location so that people from rural region can avail the services at minimum travel cost and time. There are lot of traditional schemes used by researchers in past for location allocation but this paper proposes the usage of Fuzzy C-Means clustering and BAT algorithm to optimize the location of Rural Kiosk. The meta-heuristic approach has produced better results as compared to normal graph theories in past. The experiment has been conducted on a random data set of 72 village locations from India and their clusters are formed. It is found that using only Fuzzy C-Means clustering to allocate the center and by using it in combination with BAT algorithm produced up to 25% of efficient results. This can drastically help the key stakeholders in allocation of these Rural Kiosks at right places so as to maximize their utility.
Found in: ključnih besedah
Keywords: BAT algorithm, location allocation, rural kiosks, fuzzy C-means, e-governance, tele-centers, common service centers
Published: 01.04.2021; Views: 769; Downloads: 0
.pdf Fulltext (517,82 KB)

Symmetry in the Theory of Dependence Relations
Irina Cristea, 2021, published scientific conference contribution abstract

Found in: ključnih besedah
Summary of found: ...relation, degree of influence, degree of impact, fuzzy set, hypercompositional algebra...
Keywords: dependence relation, degree of influence, degree of impact, fuzzy set, hypercompositional algebra
Published: 10.08.2021; Views: 547; Downloads: 19
.pdf Fulltext (216,43 KB)

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