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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 7 Documents
 1 
Search results for , issue "Vol 3, No 2: July 2022" : 7 Documents clear
AdMap: a framework for advertising using MapReduce pipeline Abhay Chaudhary; K R Batwada Batwada; Namita Mittal; Emmanuel S. Pilli
Computer Science and Information Technologies Vol 3, No 2: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v3i2.p82-93

Abstract

There is a vast collection of data for consumers due to tremendous development in digital marketing. For their ads or for consumers to validate nearby services which already are upgraded to the dataset systems, consumers are more concerned with the amount of data. Hence there is a void formed between the producer and the client. To fill that void, there is the need for a framework which can facilitate all the needs for query updating of the data. The present systems have some shortcomings by a vast number of information that each time lead to decision tree-based approach. A systematic solution to the automated incorporation of data into a Hadoop distributed file system (HDFS) warehouse (Hadoop file system) includes a data hub server, a generic data charging mechanism and a metadata model. In our model framework, the database would be able to govern the data processing schema. In the future, as a variety of data is archived, the datalake will play a critical role in managing that data. To order to carry out a planned loading function, the setup files immense catalogue move the datahub server together to attach the miscellaneous details dynamically to its schemas.
Exploring and comparing various machine and deep learning technique algorithms to detect domain generation algorithms of malicious variants Anoop Reddy Thatipalli; Preetham Aravamudu; K. Kartheek; Aju Dennisan
Computer Science and Information Technologies Vol 3, No 2: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v3i2.p94-103

Abstract

Domain generation algorithm (DGA) is used as the main source of script in different groups of malwares, which generates the domain names of points and will further be used for command-and-control servers. The security measures usually identify the malware but the domain name algorithms will be updating themselves in order to avoid the less efficient older security detection methods. The reason being the older detection methods does not use either the machine learning or deep learning algorithms to detect the DGAs. Thus, the impact of incorporating the machine learning and deep learning techniques to detect the DGA is well discussed. As a result, they can create a huge number of domains to avoid debar and henceforth, block the hackers and zombie systems with the older methods itself. The main purpose of this research work is to compare and analyse by implementing various machine learning algorithms that suits the respective dataset yielding better results. In this research paper, the obtained dataset is pre-processed and the respective data is processed by different machine learning algorithms such as random forest (RF), support vector machine (SVM), Naive Bayes classifier, H20 AutoML, convolutional neural network (CNN), long short-term memory neural network (LSTM) for the classification. It is observed and understood that the LSTM provides a better classification efficiency of 98% and the H20 AutoML method giving the least efficiency of 75%.
Designing the operation monitor of battery of the generator in the base transceiver station Nguyen Thị Xoan; Tran Văn Bình; Hà Ngọc Thuan; Bạch Văn Nam
Computer Science and Information Technologies Vol 3, No 2: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v3i2.p65-73

Abstract

The base transceiver station (BTS)'s continuous power supply has been the subject of a lot of research. The majority of research focuses on increasing power usage effectiveness or adding solar battery systems for BTS stations. Due to the geographical conditions in the North of Vietnam, solar cell efficiency is rather poor and the investment cost is quite high, therefore BTS stations are still relying mostly on battery and generator backup systems. This article presents a design solution to designing the operation monitor of battery of the generator in the base transceiver station. From solution design, the authors designed the hardware and software. The device allows communication and sends alarms to the base transceiver station (BTS) manager. Through the testing process, the system has met the requirements of the problem and is easily integrated into the BTS.
Self-quadplexing slot antenna for S and C-band applications Padmini Nigam; Arjuna Muduli; Sandeep Sharma; Amrindra Pal
Computer Science and Information Technologies Vol 3, No 2: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v3i2.p74-81

Abstract

This paper demonstrates a novel kind of cavity-backed self-quadplexing slot antenna for the S and C-band applications. The proposed antenna consists of 4 distinct “U”-shaped slots of different lengths and produces the quad frequency band for wireless communication systems. These slots are excited through the separate and orthogonal placed microstrip feed lines of 50 Ω; generates four distinct operating bands at 3.2 GHz, 4.1 GHz, 5.8 GHz, and 7.2 GHz. Due to the perturbation of different U-shaped slots over the substrate integrated waveguide (SIW) cavity with defined positions, the high intrinsic port isolation value is better than 30.5 dB. Thus, the proposed unique antenna structure combines the four independent operating bands with minimum mutual coupling and negligible interference among input ports, which shows the self-quadplexing feature of the antenna. The proposed antenna also has the property of frequency tunability with uni-directional radiation pattern and gain of 5.8 dBi, 5.4 dBi, 4.01 dBi, and 3.47 dBi at corresponding operated frequency. The cross-polarization is 17.3 dB and the front-to-back ratio higher than 21.5 dB at all operating quad bands. There is a good agreement between simulated |S|-parameters results and equivalent circuit model results.
Wi-Fi fingerprinting-based floor detection using adaptive scaling and weighted autoencoder extreme learning machine Atefe Alitalesi; Hamid Jazayeriy; Javad Kazemitabar
Computer Science and Information Technologies Vol 3, No 2: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v3i2.p104-115

Abstract

In practical applications, accurate floor determination in multi-building/floor environments is particularly useful and plays an increasingly crucial role in the performance of location-based services. An accurate and robust building and floor detection can reduce the location search space and ameliorate the positioning and wayfinding accuracy. As an efficient solution, this paper proposes a floor identification method that exploits statistical properties of wireless access point propagated signals to exponent received signal strength (RSS) in the radio map. Then, using single-layer extreme learning machine-weighted autoencoder (ELM-WAE) main feature extraction and dimensional reduction is implemented. Finally, ELM based classifier is trained over a new feature space to determine floor level. For the efficiency evaluation of our proposed model, we utilized three different datasets captured in the real scenarios. The evaluation result shows that the proposed model can achieve state-of-art performance and improve the accuracy of floor detection compared with multiple recent techniques. In this way, the floor level can be identified with 97.30%, 95.32%, and 96.39% on UJIIndoorLoc, Tampere, and UTSIndoorLoc datasets, respectively.
Fuzzy formal concept analysis: approaches, applications and issues Mohammed Alwersh; Kovács László
Computer Science and Information Technologies Vol 3, No 2: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v3i2.p126-136

Abstract

Formal concept analysis (FCA) is today regarded as a significant technique for knowledge extraction, representation, and analysis for applications in a variety of fields. Significant progress has been made in recent years to extend FCA theory to deal with uncertain and imperfect data. The computational complexity associated with the enormous number of formal concepts generated has been identified as an issue in various applications. In general, the generation of a concept lattice of sufficient complexity and size is one of the most fundamental challenges in FCA. The goal of this work is to provide an overview of research articles that assess and compare numerous fuzzy formal concept analysis techniques which have been suggested, as well as to explore the key techniques for reducing concept lattice size. as well as we'll present a review of research articles on using fuzzy formal concept analysis in ontology engineering, knowledge discovery in databases and data mining, and information retrieval.
Enhancing the fuzzy inference system using genetic algorithm for predicting the optimum production of a scientific publishing house Siti Kania Kushadiani; Agus Buono; Budi Nugroho
Computer Science and Information Technologies Vol 3, No 2: July 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v3i2.p116-125

Abstract

As a scientific publishing house, Indonesian Institute of Sciences (LIPI) Press' encountered some problems in publication planning, mainly predicting the optimum production of publications. This study aimed to enhance a fuzzy inference system (FIS) parameters using the genetic algorithm (GA). The enhancements led to optimally predict the number of LIPI Press publications for the following year. The predictors used were the number of work units, the number of workers, and the publishing process duration. The dataset covered a five years range of total production of LIPI Press. Firstly, an expert set up the parameters of the fuzzy inference system denoted as a FIS expert. Next, we performed a FIS GA by applying the genetic algorithm and K-fold validation in splitting the training data and testing data. The FIS GA revealed optimum prediction with parameters that were composed of both population size (30), the probability of crossover (0.75), the probability of mutation (0.01), and the number of generations (150). The experiment results show that our enhanced FIS GA outperformed FIS expert approach.

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