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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 89 Documents
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Impact of encryption and decryption techniques for high speed optical domain Rishabh Singh; Ghanendra Kumar; Chakresh Kumar
Computer Science and Information Technologies Vol 2, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v2i1.p11-15

Abstract

This project proposes the design of ultrafast communication circuit which can enable the high speed secured data transmission at 50 Gb/s and 100 Gb/s by the use of distributed Raman amplifier, erbium – doped fiber amplifier (EDFA), filter, single mode fiber along with fiber bragg grating (FBG) and attenuators. The simulation of the suggested optical circuit involves the use of parameters of Raman amplifier and EDFA and other components included in the optical structure. The design also includes the use of encryption and decryption techniques to ensure secured communication. Thus, realization of these circuits at 50 Gb/s and 100 Gb/s will enable the future optical communication applications for ultrafast data transmission to large distances.
MLGrafViz: Multilingual ontology visualization plug-in for protégé Merlin Florrence
Computer Science and Information Technologies Vol 2, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v2i1.p43-48

Abstract

Natural language processing (NLP) is rapidly increasing in all domains of knowledge acquisition to facilitate different language user. It is required to develop knowledge based NLP systems to provide better results. Knowledge based systems can be implemented using ontologies where ontology is a collection of terms and concepts arranged taxonomically. The concepts that are visualized graphically are more understandable than in the text form.  In this research paper, new multilingual ontology visualization plug-in MLGrafViz is developed to visualize ontologies in different natural languages. This plug-in is developed for protégé ontology editor. This plug-in allows the user to translate and visualize the core ontology into 135 languages.
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.
Hancitor malware recognition using swarm intelligent technique Laheeb M. Ibrahim; Maisirreem Atheeed Kamal; AbdulSattar A. Al-Alusi
Computer Science and Information Technologies Vol 2, No 3: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v2i3.p103-112

Abstract

Malware is a global risk rife designed to destroy computer systems without the owner's knowledge. It is still regarded as the most popular threat that attacks computer systems. Early recognition of unknown malware remains a problem. Swarm intelligence (SI), usually customer societies, communicate locally with their domain and with each other. Clients use very simple rules of behavior and the interactions between them lead to smart appearance, noticeable, individual behavior and optimized solution of problem and SI has been successfully applied in many fields, especially for malware ion tasks. SI also saves a considerable amount of time and enhances the precision of the malware recognition system. This paper introduces a malware recognition system for Hancitor malware using the gray wolf optimization algorithm (GWO) and artificial bee colony algorithm (ABC), which can effectively recognize Hancitor in networks.
Designing a secured audio based key generator for cryptographic symmetric key algorithms Avinash Krishnan Raghunath; Dimple Bharadwaj; M Prabhuram; Aju D
Computer Science and Information Technologies Vol 2, No 2: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v2i2.p87-94

Abstract

Cryptography is a technique to secure data transmissions and ensure confiden-tiality, authenticity and integrity of data exchanged over the digital networks by utilizing mathematical algorithms to transform the plain text (original message) to cipher text (encrypted message) using a key or seed value. The general con-sensus regarding the use of non-deterministic true random numbers (TRN) which are generated from the physical environment such as entropy keys, at-mospheric noise, etc., as a public or private key has received limited encour-agement due to the demanding hardware requirements needed to extract the necessary data from the environment. Therefore, this research aims at design-ing and developing a lightweight program to generate a true random number (TRNG) key using live audio recordings which is further randomized using system date and time. These TRNs can be used to replace the deterministic pseudo random number cryptographic keys that are presently used by indus-tries for a symmetric key encryption algorithm which devolves the algorithm to being conditionally secured. Using the audio based TRNG key would render the same encryption algorithm as unconditionally secured.
An Integrated Review On Machine Learning Approaches For Heart Disease Prediction: Direction Towards Future Research Gaps A, Fathima
Computer Science and Information Technologies List of Accepted Papers (with minor revisions)
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v3i1.p%p

Abstract

There has recently been a rapid increase in the count of statistical models obtainable for the prediction of heart disease. However, without a comprehensive overview, it remains unclear which, if any, should be applied in clinical care. Hence, this paper plans to make a clear literature review on state-of-the-art heart disease prediction models. It makes a plan to review 61 research papers and states a significant analysis. Initially, the analysis addresses the contributions of each literature works with its limitations and observes the simulation environment in which each contribution executes. Here, different types of machine learning algorithms deployed in each contribution are analyzed and state those limitations. In addition, the dataset utilized for existing heart disease prediction models are observed. Later the performance measures computed in entire papers like prediction accuracy, prediction error, specificity, sensitivity, f-measure etc are learned, and further, the best performance is also checked to confirm the effectiveness of entire contributions. Finally, comprehensive research challenges and the gap is portrayed based on the development of intelligent methods concerning the unresolved challenges in the case of heart disease prediction using data mining techniques.
Low power network on chip architectures: A survey Muhammad Raza Naqvi
Computer Science and Information Technologies Vol 2, No 3: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v2i3.p158-168

Abstract

Mostly communication now days is done through system on chip (SoC) models so, network on chip (NoC) architecture is most appropriate solution for better performance. However, one of major flaws in this architecture is power consumption. To gain high performance through this type of architecture it is necessary to confirm power consumption while designing this. Use of power should be diminished in every region of network chip architecture. Lasting power consumption can be lessened by reaching alterations in network routers and other devices used to form that network. This research mainly focusses on state-of-the-art methods for designing NoC architecture and techniques to reduce power consumption in those architectures like, network architecture, network links between nodes, network design, and routers.
Classification of mammograms based on features extraction techniques using support vector machine Enas Mohammed Hussein Saeed; Hayder Adnan Saleh; Enam Azez Khalel
Computer Science and Information Technologies Vol 2, No 3: November 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v2i3.p121-131

Abstract

Now mammography can be defined as the most reliable method for early breast cancer detection. The main goal of this study is to design a classifier model to help radiologists to provide a second view to diagnose mammograms. In the proposed system medium filter and binary image with a global threshold have been applied for removing the noise and small artifacts in the pre-processing stage. Secondly, in the segmentation phase, a hybrid bounding box and region growing (HBBRG) algorithm are utilizing to remove pectoral muscles, and then a geometric method has been applied to cut the largest possible square that can be obtained from a mammogram which represents the ROI. In the features extraction phase three method was used to prepare texture features to be a suitable introduction to the classification process are first order (statistical features), local binary patterns (LBP), and gray-level co-occurrence matrix (GLCM), finally, SVM has been applied in two-level to classify mammogram images in the first level to normal or abnormal, and then the classification of abnormal once in the second level to the benign or malignant image. The system was tested on the MAIS the Mammogram image analysis Society (MIAS) database, in addition to the image from the Teaching Oncology Hospital, Medical City in Baghdad, where the results showed achieving an accuracy of 95.454% for the first level and 97.260% for the second level, also, the results of applying the proposed system to the MIAS database alone were achieving an accuracy of 93.105% for the first level and 94.59 for the second level.
Numerical approach for extraction of photovoltaic generator single-diode model parameters Abdelaaziz Benahmida; Noureddine Maouhoub; Hassan Sahsah
Computer Science and Information Technologies Vol 2, No 2: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v2i2.p58-66

Abstract

In this work, a numerical approach has been proposed to estimate the five single-diode circuit model physical parameters of photovoltaic generators from their experimental current-voltage characteristics. Linear least square method has been used to solve the system of three linear equations to express the shunt resistance, the saturation current and the photocurrent as a function of the series resistance and the ideality factor. Two key points have been used to solve the system of two nonlinear equations to extract values of series resistance and ideality factor. The advantage of the proposed method with respect of existing numerical techniques is that use only two key points of the experimental characteristic and need only two initial guesses and does not use any approximation. To evaluate the proposed method, three PV generators data have been used to compare the experimental and the theoretical curves. The application of the proposed method provides a good agreement with the experimental.

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