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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 12 Documents
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Search results for , issue "Vol 4, No 1: March 2023" : 12 Documents clear
Plant disease detection based on image processing and machine learning techniques Swati A. Avinash Bhavsar; Varsha H. Patil; Abolee H. Patil
Computer Science and Information Technologies Vol 4, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i1.p%p

Abstract

In the field of agriculture, prevention and control of plant disease are very important. The diseases can be controlled at an early stage by rapid and accurate diagnostics of the same, which could help control the disease at its initial stage. The automatic technique for plant disease detection helps reduce the need for meticulous individual plant monitoring on the farm. A combination of machine learning and image processing may help in plant disease recognition. The proposed technique is based on a combination of the abovementioned techniques, where for extracting leaf image features such as color, texture, and intensity, the G Gabor filter and watershed segmentation algorithms are used. Along with this classification, techniques are used for identifying the disease. The proposed algorithms' results are compared with those of standard state-of-the-art techniques.
Comparative study of ensemble deep learning models to determine the classification of turtle species Ruvita Faurina; Andang Wijanarko; Aknia Faza Heryuanti; Sahrial Ihsani Ishak; Indra Agustian
Computer Science and Information Technologies Vol 4, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i1.p24-32

Abstract

Sea turtles are reptiles listed on the international union for conservation of nature (IUCN) red list of threatened species and the convention on international trade in endangered species of wild fauna and flora (CITES) Appendix I as species threatened with extinction. Sea turtles are nearly extinct due to natural predators and people who are frequently incorrect or even ignorant in determining which turtles should not be caught. The aim of this study was to develop a classification system to help classify sea turtle species. Therefore, the ensemble deep learning of convolutional neural network (CNN) method based on transfer learning is proposed for the classification of turtle species found in coastal communities. In this case, there are five well-known CNN models (VGG-16, ResNet-50, ResNet-152, Inception-V3, and DenseNet201). Among the five different models, the three most successful were selected for the ensemble method. The final result is obtained by combining the predictions of the CNN model with the ensemble method during the test. The evaluation result shows that the VGG16 - DenseNet201 ensemble is the best ensemble model, with accuracy, precision, recall, and F1-Score values of 0.74, 0.75, 0.74, and 0.76, respectively. This result also shows that this ensemble model outperforms the original model.
Optimization of bakery production by using branch and bound approach Rahimullaily Rahimullaily; Rahmadini Darwas; Ratih Purwasih
Computer Science and Information Technologies Vol 4, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i1.p50-58

Abstract

Mommy Ai Kitchen is one the businesses specializing in the bakery business, producing cupcakes, birthday cakes, brownies, and donuts. However, it does not optimally determine each bakery’s production quantity, so it offers fewer profits and becomes a problem. This research aims to find the optimal production quantity so that this business maximizes profits. The method used was integer programming using the branch and bound approach, which counts the decision variable value using the simplex method. This research was based on the number of raw materials on hand-wheat flour, sugar, eggs, modal, and the profits of each bakery. Based on the analysis of the branch and bound approach, it was known that the maximum profit value was IDR 253,200, with eight alternative options for the bakeries that were produced. One of them was Mommy Ai Kitchen, which could produce three cupcakes, five birthday cakes, one brownie, and nine donuts to get that maximum profit. Meanwhile, Mommy Ai Kitchen’s estimation could produce one cupcake, one brownie, and six donuts using available materials with a profit of IDR 78,800. As a result, the profit difference before and after integer programming was IDR 174,400.
Key-frame extraction based video watermarking using speeded up robust features and discrete cosine transform Bhagyashri S. Kapre; Archana M. Rajurkarb
Computer Science and Information Technologies Vol 4, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i1.p85-94

Abstract

Due to advancements in the internet and multimedia technologies, unauthorised users can easily modify video content. As a result, video authentication has been established as a viable solution for ensuring multimedia security. We propose a key-frame based video watermarking scheme based on discrete cosine transform (DCT). First, the pearson correlation coefficient (PCC) is used to detect the shot boundaries of the input video. To reduce the difficulties created by traditional video watermarking systems; an entropy measure is employed to detect key-frames from input video. Traditional schemes entail embedding the entire watermark into all frames of the video, which is inefficient and time-consuming. To improve the security, robustness, and imperceptibility of the proposed video watermarking scheme, speeded up robust feature points are extracted from each key-frame of the shot and used as reference points for embedding and detection of watermark. The embedded watermark is extracted blindly without using the original data during the extraction process. The results of the experiments reveal that the proposed technique effectively detects shot boundaries under a variety of camera operations and outperforms in terms of imperceptibility and resilience.
Fine grained irony classification through transfer learning approach Abhinandan Shirahatti; Vijay Rajpurohit; Sanjeev Sannakki
Computer Science and Information Technologies Vol 4, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i1.p43-49

Abstract

Nowadays irony appears to be pervasive in all social media discussion forums and chats, offering further obstacles to sentiment analysis efforts. The aim of the present research work is to detect irony and its types in English tweets We employed a new system for irony detection in English tweets, and we propose a distilled bidirectional encoder representations from transformers (DistilBERT) light transformer model based on the bidirectional encoder representations from transformers (BERT) architecture, this is further strengthened by the use and design of bidirectional long-short term memory (Bi-LSTM) network this configuration minimizes data preprocessing tasks proposed model tests on a SemEval-2018 task 3, 3,834 samples were provided. Experiment results show the proposed system has achieved a precision of 81% for not irony class and 66% for irony class, recall of 77% for not irony and 72% for irony, and F1 score of 79% for not irony and 69% for irony class since researchers have come up with a binary classification model, in this study we have extended our work for multiclass classification of irony. It is significant and will serve as a foundation for future research on different types of irony in tweets.
Selected advanced themes in ethical hacking and penetration testing Buthayna AlSharaa; Saed Thuneibat; Rawan Masadeh; Mohammad Alqaisi
Computer Science and Information Technologies Vol 4, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i1.p69-75

Abstract

Since 1980 cyberattacks have been evolving with the rising numbers of internet users and the constant evolving of security systems, and since then security systems experts have been trying to fight these kinds of attacks. This paper has both ethical and scientific goals, ethically, to raise awareness on cyberattacks and provide people with the knowledge that allows them to use the world wide web with fewer worries knowing how to protect their information and their devices with what they can. Scientifically, this paper includes a deep understanding of types of hackers, attacks, and various ways to stay safe online. This research investigates how ethical hackers adapt to the current and upcoming cyber threats. The different approaches for some famous hacking types along with their results are shown. Python and Ruby are used for coding, which we run on Kali Linux operating system.
Antispoofing in face biometrics: A comprehensive study on software-based techniques Vinutha H; Thippeswamy G
Computer Science and Information Technologies Vol 4, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i1.p1-13

Abstract

The vulnerability of the face recognition system to spoofing attacks has piqued the biometric community's interest, motivating them to develop anti-spoofing techniques to secure it. Photo, video, or mask attacks can compromise face biometric systems (types of presentation attacks). Spoofing attacks are detected using liveness detection techniques, which determine whether the facial image presented at a biometric system is a live face or a fake version of it. We discuss the classification of face anti-spoofing techniques in this paper. Anti-spoofing techniques are divided into two categories: hardware and software methods. Hardware-based techniques are summarized briefly. A comprehensive study on software-based countermeasures for presentation attacks is discussed, which are further divided into static and dynamic methods. We cited a few publicly available presentation attack datasets and calculated a few metrics to demonstrate the value of anti-spoofing techniques.
The effect of segmentation on the performance of machine learning methods on the morphological classification of Friesien Holstein dairy cows Amril Mutoi Siregar; Yohanes Aris Purwanto; Sony Hartono Wijaya; Nahrowi Nahrowi
Computer Science and Information Technologies Vol 4, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i1.p59-68

Abstract

Many classification algorithms are in the form of image pattern recognition; the approach to the complexity of the problem should be a feature of feasibility for representing images. The morphology of dairy cows greatly affects their health and milk production. The paper will apply several classification methods based on the morphology of Holstein Friesian dairy cows. To improve the accuracy of the model used, the segmentation process is the right step. In this paper, we compare several machine learning algorithms to get optimal accuracy. The algorithm used a support vector machine (SVM), artificial neural networks, random forests and logistic regression. Segmentation methods used are mask region-based convolutional neural network (R-CNN) and Canny; optimal accuracy is expected to create intelligent applications. The success of the method is measured with accuracy, precision, recall, and F1 Score, as well as testing by conducting a training-testing ratio of 90:10 and 80:20. This study discovered an artificial neural network optimal model with Canny with an accuracy of 82.50%, precision of 87.00%, recall of 79.00%, F1-score of 81.62%, and testing ratio of 90:10. While the models with the highest 80:20 ratio achieved 84.39% accuracy, 88.46% precision, 80.47%, and 83.00% F1-score with mask R-CNN with logistic regression.
Fraudulent transactions detection on cryptocurrency blockchain: a machine learning approach Anoop Reddy Thatipalli; Vijayakumar Kuppusamy
Computer Science and Information Technologies Vol 4, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i1.p%p

Abstract

Blockchain technologies have gained a huge amount of importance in recent years, and the use of the blockchain concept in cryptocurrency transactions has always gained the faith of industrial standards. Ethereum is a blockchain platform that allows customers to conduct cryptocurrency transactions, which are then used to build and deploy the API using smart contracts. Blockchain can be used to change the value of money in crypto exchanges and banking systems. even though the blockchain system is consistent and reliable. Attackers still try to steal the money by executing well-known techniques like Ponzi scheme attacks or by using malware software. As the participants in the Ethereum platform are "anonymous," users can access multiple accounts under the same hash identity. As a result, it will be difficult to find the malicious users who are contributing to the fraudulent activities. Although activities such as Ponzi schemes are to be monitored by the authority in order to keep the API safe from scammers and the platform legitimate, In this paper, we detect malicious transaction nodes with the help of machine learning-based anomaly detection and also give the structural architecture for creating a secure API wallet, which solves the basic security protection from Ponzi-scheme multiple identity attacks by introducing KYC contracts in smart contracts of the Ethereum platform such that no duplicate users can misuse the cryptocurrencies. In this case, we use two different machine learning models that detect with a 95.24% accuracy and a 0.88% false positive ratio. and then we compare the capabilities of random forest and support vector machine classifiers to identify the anomaly-based accounts, which are on datasets of around 300 accounts. By introducing HD-Wallets for API, we show the rules for digital wallets and cryptocurrency transactions that protect against malware.
The antecedent e-government quality for public behaviour intention, and extended expectation-confirmation theory Taqwa Hariguna; Untung Rahardja; Qurotul Aini
Computer Science and Information Technologies Vol 4, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v4i1.p33-42

Abstract

An The main objective of the study is to identify the antecedent of leadership quality, public satisfaction and public behaviour intention of e-government service. Also, this study integrated e-government quality to expectation-confirmation model. In order to achieve these goals, observational research was then carried out to collect primary information, using the method of data dissemination and obtaining the opinion of 360 from the public using the e-government service and some of the e-government and software quality experts. The results of the study show that the positive association among the e-government services quality and public perceived usefulness, public expectation confirmation, leadership quality and public satisfaction that also play a positive role on the public behavior intention.

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