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As cloud computing is becoming part of our life day by day, it has attracted research community to tackle the research problems of cloud computing environment. Virtual machine placement is a brewing area for cloud researchers so in the proposed model virtual machine placement problem is modelled as an optimization problem with the objective of resource wastage. As huge resource wastage can affect the cloud service provider so, an virtual machine placement algorithm based on interactive artificial bee colony was proposed. The performance of the proposed method is thoroughly compared with other competing algorithms through exhaustive experiments and results are presented.

Virtual Machine, Cloud Computing, Artificial Bee Colony, Resource Wastage, Optimization

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[2] A. Tripathi, I. Pathak, and D. Prakash, “Modified Dragonfly Algorithm for Optimal Virtual Machine Placement in Cloud Computing,” pp. 1316-1342, 2020
[3] S. Azizi, M. Shojafar, and S. Member, “GRVMP?: A Greedy Randomized Algorithm for Virtual Machine Placement in Cloud Data Centers,” pp. 1–12, 2020.
[4] W. Zhang, X. Chen, and J. Jiang, “A Multi-Objective Optimization Method of Initial Virtual Machine Fault-Tolerant Placement for Star Topological Data Centers of Cloud Systems,” vol. 26, no. 1, pp. 95–111, 2021.
[5] X. Fu and C. Zhou, “Predicted Affinity Based Virtual Machine Placement in Cloud Computing Environments,” IEEE Trans. Cloud Comput., vol. 8, no. 1, pp. 246–255, 2020.
[6] M. Ghetas, “A multi-objective Monarch Butterfly Algorithm for virtual machine placement in cloud computing,” Neural Comput. Appl., vol. 33, no. 17, pp. 11011–11025, 2021.
[7] S. Gharehpasha, M. Masdari, and A. Jafarian, "Virtual machine placement in cloud data centers using a hybrid multi-verse optimization algorithm," vol. 54, no. 3. Springer Netherlands, 2021.
[8] D. Alboaneen, H. Tianfield, Y. Zhang, and B. Pranggono, “A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers,” Futur. Gener. Comput. Syst., vol. 115, pp. 201–212, 2021.
[9] B. Zhang, X. Wang, and H. Wang, “Virtual machine placement strategy using cluster-based genetic algorithm,” Neurocomputing, vol. 428, pp. 310–316, 2021.
[10] Shalu and D. Singh, “Artificial neural network-based virtual machine allocation in cloud computing,” pp. 1-12, 2021.
[11] K. M. B. S. V. Bhanu, “A multi-objective krill herd algorithm for virtual machine placement in cloud computing,” vol. 76, no. 6, pp. 4525-4542, 2018.
[12] A. Ponraj, “Optimistic Virtual Machine Placement in Cloud Data Centers using Queuing Approach,” vol. 93, pp. 338-344, 2018.
[13] W. Yao, Y. Shen, and D. Wang, “A Weighted PageRank-Based Algorithm for Virtual Machine Placement in Cloud Computing,” IEEE Access, vol. 7, pp. 176369–176381, 2019.
[14] Z. Zhou, M. Shojafar, M. Alazab, J. Abawajy, and F. Li, “AFED-EF: An Energy-Efficient VM Allocation Algorithm for IoT Applications in a Cloud Data Center,” IEEE Trans. Green Commun. Netw., vol. 5, no. 2, pp. 658–669, 2021.
[15] X. Yu, W. Chen, and X. Zhang, “An Artificial Bee Colony Algorithm for Solving Constrained Optimization Problems,” Proc. 2018 2nd IEEE Adv. Inf. Manag. Commun. Electron. Autom. Control Conf. IMCEC 2018, pp. 2663–2666, 2018.
[16] I. Pathak and D. P. Vidyarthi, “An Interactive Artificial Bee Colony based Virtual Network Embedding,” pp. 1-6, 2015.
[17] P. W. Tsai, M. K. Khan, J. S. Pan, and B. Y. Liao, “Interactive artificial bee colony supported passive continuous authentication system,” IEEE Syst. J., vol. 8, no. 2, pp. 395–405, 2014.
[18] A. Tripathi, I. Pathak, and D. P. Vidyarthi, “Energy Efficient VM Placement for Effective Resource Utilization using Modified Binary PSO,” Comput. J., vol. 61, no. 6, pp. 832–846, 2018.

Shubham Kumar, Atul Tripathi, "Virtual Machine Placement using Interactive Artificial Bee Colony Algorithm(VMPIABC)," International Journal of Computer Sciences and Engineering, Vol.9, Issue.10, pp.1-6, 2021.

This study aims to develop a multi-criteria decision-making (MCDM) based framework for evaluation, ranking and structured comparison of the sustainability practices in the Yemeni hotels. To achieve this goal, the performance criteria of green hotels ranking and classification problems with the Yemeni context are discussed, some possible applications of MCDM technics are illustrated, three most common approaches (AHP, Fuzzy AHP, and FDM) were selected, and implemented for ranking and classification of the eco-hotels performance criteria, a case study on the impact of their application on a ranking and classification decisions was conducted, three possible multi-functional frameworks were obtained. The required consistency and criteria acceptability tests for each implemented method were examined. The sensitivity analysis, total number of the position`s shifts of the ranked criteria, the overall level of change in them, and the Pearson coefficient are used to compare the results obtained by all methods, and to select the best and less sensitive evaluation framework. The result shows that different variants of MCDM methods leads to the same classification’s and different ranking`s result. A Very high level of numerical correlation coefficients, low degree of sensitivity and very small change level in the positions of the ranked criteria were observed between results defined by the Fuzzy AHP method and those which were obtained by the AHP and FDM methods. Accordingly, a new more accurate, and more relevant to the Yemeni reality fuzzy based and multi-functional framework was developed. Study suggests the application of this framework for further sustainable planning practices in Yemen.

MCDM, Comparative analysis, Eco-hotels, Sustainability, Prioritization, Green hotels, AHP, F-AHP, FDM

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Adel A. Nasser, M.M. Saeed, Mijahed N. Aljober, "Application of Selected MCDM Methods for Developing a Multi-Functional Framework for Eco-Hotel Planning in Yemen," International Journal of Computer Sciences and Engineering, Vol.9, Issue.10, pp.7-18, 2021.

Companies often use relational database management systems (RDBMS) such as Oracle and Inform mix, to store their data persistently. The database technology developed and deployed in RDBMS is relatively mature. Besides efficient storage and retrieval, this technology provides many additional features such as concurrency control, recoverability, and high availability. Thirdly, the rigid structure of relational data makes it amenable to complex queries and analysis such as on-line analytical processing (OLAP), the predecessor of data mining. There are many different techniques and algorithms for relational data that can be classified as data mining. There are roughly four broad classes i.e. clustering, classification, sequence analysis, and associations. We consider data mining for structured data from a database perspective. As a consequence in association rules will be featured more prominently than the other three classes of mining problems. Query flocks are an elegant framework for a large class of data mining problems over relational data. The main features of query flocks are declarative formulation of a large class of mining queries. Systematic optimization and processing of such queries Integration with relational DBMS, taking full advantage of existing capabilities. This paper focus mainly on the declarative formulation of mining problems as query flocks.

RDBMS, Clustering, Query flocks, Query Optimization, Relational data, Classification, Clustering, sequence Analysis

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N. BhanuPrakash, E. Kesavulu Reddy, "Query Processing In Text Mining," International Journal of Computer Sciences and Engineering, Vol.9, Issue.10, pp.19-23, 2021.

Web look inquiries are regularly questionable or multi-faceted, which makes a straightforward positioned rundown of results deficient. To help data finding for such faceted inquiries, we investigate a system that unequivocally speaks to intriguing aspects of an inquiry utilizing gatherings of semantically related terms separated from list items. For instance, for the inquiry "stuff remittance", these gatherings may be distinctive aircrafts, diverse flight types (household, global), or diverse travel classes (first, business, economy). We name these gatherings inquiry aspects and the terms in these gatherings feature terms. We build up a regulated methodology dependent on a graphical model to perceive inquiry features from the boisterous hopefuls found. The graphical model figures out how likely a competitor term is to be a feature term just as how likely two terms are to be assembled together in a question aspect, and catches the conditions between the two elements. We propose two calculations for estimated surmising on the graphical model since correct derivation is immovable. Our assessment consolidates review and exactness of the aspect terms with the gathering quality. Trial results on an example of web questions demonstrate that the directed technique fundamentally beats existing methodologies, which are generally unsupervised, proposing that inquiry feature extraction can be adequately learned.

Query, Facet, Faceted Search, Query Suggestion, Query Reformulation, Query Summarization

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N. Bhanu Prakash, E. Kesavulu Reddy, "Judgment Robotically Mining Facets for Requests from Their Exploration Consequences," International Journal of Computer Sciences and Engineering, Vol.9, Issue.10, pp.24-27, 2021.

Leukemia is a cancerous disease characterised by an uncontrollable development of abnormal White Blood Cells (WBC). The identification of acute leukaemia is based on the percentage of WBC in the peripheral blood. In practice, the manual microscopic examination methods are used for acute leukemia detection. Despite the use of hardware autofocus mechanisms, large image collections acquired by automated microscopes often contain some fraction of low quality, out-of-focus images. More complicated cell morphology, with a wide range of size, border, position, and colour contrast were also obtained. Moreover, when the images are captured, the contrast between the cell border and the background in peripheral blood smears is influenced by the lighting position, and the effects of unwanted noise on blood leukemia images can results .in inaccurate diagnosis. So, an efficient pre-processing method is required to highlights the edges of nuclei. This paper describes in detail about the proposed Image Pre-Processing Techniques with Deep Learning Method for Detecting Leukemia in Microscopic Blood Images. This automated system will detect leukemia cells from the blood cancer affected patient’s collected blood sample. The image processing techniques used for the diagnosis include optimized contrast stretching (OCS) to enhance the image and detect the nuclei, also the k-means clustering algorithm for nuclei segmentation. A features extraction based on geometry, colour, texture, and statistics information are extracted, as well as fuzzy rule based decision system are performed to get better results of leukemia detection.

Leukemia, microscopic examination, Deep Learning method, contrasts stretching, k-means clustering

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R. Suriyagrace, M. Devapriya, "Effective Image Pre-Processing Techniques with Deep Learning for Leukemia Detection," International Journal of Computer Sciences and Engineering, Vol.9, Issue.10, pp.28-36, 2021.

Today, Vehicular Ad Hoc Network (VANET) is an emerging technology. Mobility management is the emerging research area in the field of VANET for supporting different aspects of Intelligent Transportation System (ITS) applications. Mobility solutions for VANET can be classified into two categories namely, inter domain and intra domain. Therefore, the mobility management for vehicular networks is required. However most of Mobility model currently used are very simple. In this paper we will focus on the Network Mobility Approach in Vehicular Ad Hoc Network, this model is well suited for highway and city environment, and provides efficient and timely route establishment between the moving vehicles from one network to another network.

VANETs, VANEMO, Access Router and IEEE802.11p

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Chandrakant Kumar Singh, Narendra Kumar Shukla, "A Seamless Handover Management for VANETs across Heterogeneous Networks," International Journal of Computer Sciences and Engineering, Vol.9, Issue.10, pp.37-40, 2021.