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Computer Science and Information Technologies
Published by Institute of Advanced Engineering and Science
ISSN : 2722323X     EISSN : 27223221     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Computer Science and Information Technologies ISSN 2722-323X, e-ISSN 2722-3221 is an open access, peer-reviewed international journal that publish original research article, review papers, short communications that will have an immediate impact on the ongoing research in all areas of Computer Science/Informatics, Electronics, Communication and Information Technologies. Papers for publication in the journal are selected through rigorous peer review, to ensure originality, timeliness, relevance, and readability. The journal is published four-monthly (March, July and November).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 6 Documents
 1 
Search results for , issue "Vol 1, No 2: July 2020" : 6 Documents clear
Insult detection using a partitional CNN-LSTM model Mohamed Maher Ben Ismail
Computer Science and Information Technologies Vol 1, No 2: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v1i2.p84-92

Abstract

Recently, deep learning has been coupled with notice-able advances in Natural Language Processing related research. In this work, we propose a general framework to detect verbal offense in social networks comments. We introduce a partitional CNN-LSTM architecture in order to automatically recognize verbal offense patterns in social network comments. Specifically, we use a partitional CNN along with a LSTM model to map the social network comments into two predefined classes. In particular, rather than considering a whole document/comments as input as performed using typical CNN, we partition the comments into parts in order to capture and weight the locally relevant information in each partition. The resulting local information is then sequentially exploited across partitions using LSTM for verbal offense detection. The combination of the partitional CNN and LSTM yields the integration of the local within comments information and the long distance correlation across comments. The proposed approach was assessed using real dataset, and the obtained results proved that our solution outperforms existing relevant solutions.
High security mechanism: fragmentation and replication in the cloud with auto update in the system Shrutika Khobragade; Rohini Bhosale; Rahul Jiwane
Computer Science and Information Technologies Vol 1, No 2: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v1i2.p78-83

Abstract

Cloud Computing makes immense use of internet to store a huge amount of data. Cloud computing provides high quality service with low cost and scalability with less requirement of hardware and software management. Security plays a vital role in cloud as data is handled by third party hence security is the biggest concern to matter. This proposed mechanism focuses on the security issues on the cloud. As the file is stored at a particular location which might get affected due to attack and will lost the data. So, in this proposed work instead of storing a complete file at a particular location, the file is divided into fragments and each fragment is stored at various locations. Fragments are more secured by providing the hash key to each fragment. This mechanism will not reveal all the information regarding a particular file even after successful attack. Here, the replication of fragments is also generated with strong authentication process using key generation. The auto update of a fragment or any file is also done here. The concept of auto update of filles is done where a file or a fragment can be updated online. Instead of downloading the whole file, a fragment can be downloaded to update. More time is saved using this methodology.
Building a multilingual ontology for education domain using monto method Merlin Florrence
Computer Science and Information Technologies Vol 1, No 2: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v1i2.p47-53

Abstract

Ontologies are emerging technology in building knowledge based information retrieval systems. It is used to conceptualize the information in human understandable manner. Knowledge based information retrieval are widely used in the domain like education, artificial intelligence, healthcare and so on. It is important to provide multilingual information of those domains to facilitate multilanguage users. In this paper, we propose a multilingual ontology (MOnto) methodology to develop multilingual ontology applications for education domain. New algorithms are proposed for merging and mapping multilingual ontologies.
On the performance of switching methods in space division multiplexing based optical networks Sridhar Iyer
Computer Science and Information Technologies Vol 1, No 2: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v1i2.p54-60

Abstract

In the current work, for space division multiplexing based optical networks (SDM-b-OTNs), we investigate the performance of various switching methods with a variation in traffic evolution over different time frame periods. Initially, comparison of the existing methods viz., independent switching (InSw), frequency switching (FqSw), and space switching (SpSw) demonstrates that (i) over longer periods of time frame, FqSw provisions low network usage, and (ii) SpSw offers low network usage for shorter periods of time frame; however, as time frame increases to longer periods, SpSw starts to outperform InSw. Next, we investigate a hybrid switching (HySw) method which begins by implementing InSw and then shifts to the use of SpSw after the activation of specific numbers of space channels. The simulation results demonstrate that HySw provisions substantial savings on the costs incurred for switching, and with lower space channel values it also offers a balance in the trade-off which occurs between the costs associated for activating the space channels and that incurred for switching.
Feature analysis of ontology visualization methods and tools Merlin Florrence Joseph; Ravi Lourdusamy
Computer Science and Information Technologies Vol 1, No 2: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v1i2.p61-77

Abstract

Visualization is a technique of creating images, graphs or animations to share knowledge. Different kinds of visualization methods and tools are available to envision the data in an efficient way. The visualization tools and techniques enable the user to understand the knowledge in an easy manner. Nowadays most of the information is presented semantically which provides knowledge based retrieval of the information. Knowledge based visualization tools are required to visualize semantic concepts. This article analyses the existing semantic based visualization tools and plug-ins. The features and characteristics of these tools and plug-ins are analyzed and tabulated.
Efficient and scalable multitenant placement approach for in-memory database over supple architecture Arpita Shah; Narendra Patel
Computer Science and Information Technologies Vol 1, No 2: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v1i2.p39-46

Abstract

Of late Multitenant model with in-memory database has become prominent area for research. The paper has used advantages of multitenancy to reduce the cost for hardware, labor and make availability of storage by sharing database memory and file execution. The purpose of this paper is to give overview of proposed Supple architecture for implementing in memory database backend and multitenancy, applicable in public and private cloud settings. Backend in-memory database uses column-oriented approach with dictionary based compression technique. We used dedicated sample benchmark for the workload processing and also adopt the SLA penalty model. In particular, we present two approximation algorithms, multitenant placement (MTP) and best-fit greedy to show the quality of tenant placement. The experimental results show that multi-tenant placement (MTP) algorithm is scalable and efficient in comparison with best fit greedy algorithm over proposed architecture.

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