International Journal of Informatics and Communication Technology (IJ-ICT)
International Journal of Informatics and Communication Technology (IJ-ICT) is a common platform for publishing quality research paper as well as other intellectual outputs. This Journal is published by Institute of Advanced Engineering and Science (IAES) whose aims is to promote the dissemination of scientific knowledge and technology on the Information and Communication Technology areas, in front of international audience of scientific community, to encourage the progress and innovation of the technology for human life and also to be a best platform for proliferation of ideas and thought for all scientists, regardless of their locations or nationalities. The journal covers all areas of Informatics and Communication Technology (ICT) focuses on integrating hardware and software solutions for the storage, retrieval, sharing and manipulation management, analysis, visualization, interpretation and it applications for human services programs and practices, publishing refereed original research articles and technical notes. It is designed to serve researchers, developers, managers, strategic planners, graduate students and others interested in state-of-the art research activities in ICT.
Articles
462 Documents
<![CDATA[Prediction analysis on the pre and post COVID outbreak assessment using machine learning and deep learning ]]>
<![CDATA[Ishaan Walecha]]>;
<![CDATA[Divya Jain]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 11, No 2: August 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v11i2.pp140-147
<![CDATA[In this time of a global urgency where people are losing lives each day in a large number, people are trying to develop ways/technology to solve the challenges of COVID-19. Machine learning (ML) and artificial intelligence (AI) tools have been employed previously as well to the times of pandemic where they have proven their worth by providing reliable results in varied fields this is why ML tools are being used extensively to fight this pandemic as well. This review describes the applications of ML in the post and pre COVID-19 conditions for contact tracing, vaccine development, prediction and diagnosis, risk management, and outbreak predictions to help the healthcare system to work efficiently. This review discusses the ongoing research on the pandemic virus where various ML models have been employed to a certain data set to produce outputs that can be used for risk or outbreak prediction of virus in the population, vaccine development, and contact tracing. Thus, the significance and the contribution of ML against COVID-19 are self-explanatory but what should not be compromised is the quality and accuracy based on which solutions/methods/policies adopted or produced from this analysis which will be implied in the real world to real people.]]>
<![CDATA[Model Online Industrial Technology Programs ]]>
<![CDATA[Raj L Desai]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 11, No 2: August 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v11i2.pp%p
<![CDATA[This paper outlines a proposal for serving working professionals in industry by offering them two programs of study to enhance their education and improve their job prospects. The programs were specifically designed to meet the needs of the manufacturing industry in Texas. The courses are proposed to be offered online because we expected working people to be interested in these programs. By offering courses online we can attract a new group of working students that are constrained by their jobs from being able to take regular courses]]>
<![CDATA[Cyber attack awareness and prevention in network security ]]>
<![CDATA[Zolkipli Mohamad Fadli]]>;
<![CDATA[Shu See Yong]]>;
<![CDATA[Low Kai Kee]]>;
<![CDATA[Gan Hui Ching]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 11, No 2: August 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v11i2.pp105-115
<![CDATA[This article aims to provide an overview of cyber attack awareness and prevention in network security. This article discussed the different types of cyber attacks, current trends of cyber attacks, how to prevent cyber attacks and uum students' awareness of cyber attacks. First, we will go over the different types of cyber attack, current trend, impact of cyber attack and the prevention. The approach entailed comparing and observing the outcomes of 13 different papers. The survey's findings would demonstrate the results obtained after analyzing the data collection which are the questionnaire filled out by respondents after watching the cyber attack awareness video to improve awareness of students through the cyber attack. Depending on the outcome of this survey, we will have a better understanding of current students' knowledge and awareness of cyber attacks, allowing us to improve students' understanding of cyber threats and the necessity of cyber security.]]>
<![CDATA[Meliorating usable document density for online event detection ]]>
<![CDATA[Manisha Samanta]]>;
<![CDATA[Yogesh Kumar Meena]]>;
<![CDATA[Arka Prokash Mazumdar]]>;
<![CDATA[Girdhari Singh]]>;
<![CDATA[Dinesh Gopalani]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 11, No 2: August 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v11i2.pp85-95
<![CDATA[Online event detection (OED) has seen a rise in the research community as it can provide quick identification of possible events happening at times in the world. Through these systems, potential events can be indicated well before they are reported by the news media, by grouping similar documents shared over social media by users. Most OED systems use textual similarities for this purpose. Similar documents, that may indicate a potential event, are further strengthened by the replies made by other users, thereby improving the potentiality of the group. However, these documents are at times unusable as independent documents, as they may replace previously appeared noun phrases with pronouns, leading OED systems to fail while grouping these replies to their suitable clusters. In this paper, a pronoun resolution system that tries to replace pronouns with relevant nouns over social media data is proposed. Results show significant improvement in performance using the proposed system.]]>
<![CDATA[A cluster and association analysis visualization using Moodle activity log data ]]>
<![CDATA[Andri Reimondo Tamba]]>;
<![CDATA[Krista Lumbantoruan]]>;
<![CDATA[Aulia Pakpahan]]>;
<![CDATA[Samuel Situmeang]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 2: August 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v12i2.pp150-161
<![CDATA[The course activity log is where a learning management system (LMS) like Moodle keeps track of the various learning activities. In order to conduct a quicker and more in-depth examination of the students' behaviors, the instructor may either directly examine the log or make use of more complex methodologies such as data mining. The majority of the proposed methods for analyzing this log data center mostly on predictive analysis. In this research, cluster analysis and association analysis, two separate data mining functions, are investigated in order to analyze the log. The students' activities are used in the cluster analysis performed with K-Means++, and the association analysis performed with Apriori is used to investigate the connections between the students' various activities. A dashboard presentation of the findings is provided in order to facilitate clearer comprehension. Based on the findings of the analysis, it can be concluded that the structure of the student cluster is medium, whereas the association between the activities undertaken by students is positively correlated and well-balanced. The subjective review of the dashboard reveals that the visualization is already sufficient, but there are some recommendations for making it even better.]]>
<![CDATA[Review-based analysis of clustering approaches in a recommendation system ]]>
<![CDATA[Hera, Sabeena Yasmin]]>;
<![CDATA[Amjad, Mohammad]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 1: April 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v13i1.pp1-8
<![CDATA[Because of the explosion in data, it is now incredibly difficult for a single person to filter through all of the information and extract what they need. As a result, information filtering algorithms are necessary to uncover meaningful information from the massive amount of data already available online. Users can benefit from recommendation systems (RSs) since they simplify the process of identifying relevant information. User ratings are incredibly significant for creating recommendations. Previously, academics relied on historical user ratings to predict future ratings, but today, consumers are paying more attention to user reviews because they contain so much relevant information about the user's decision. The proposed approach uses written testimonials to overcome the issue of doubt in the ratings' pasts. Using two data sets, we performed experimental evaluations of the proposed framework. For prediction, clustering algorithms are used with natural language processing in this strategy. It also evaluates the findings of various methods, such as the K-mean, spectral, and hierarchical clustering algorithms, and offers conclusions on which strategy is optimal for the supplied use cases. In addition, we demonstrate that the proposed technique outperforms alternatives that do not involve clustering.]]>
<![CDATA[Comparison of three common software-defined network controllers ]]>
<![CDATA[Rikie Kartadie]]>;
<![CDATA[Edy Prayitno]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 2: August 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v12i2.pp85-91
<![CDATA[The software-defined network (SDN) controller adds and removes the contents of the flow table through secure channels to determine how packets are processed and how the flow table is managed. The controller pays attention to network intelligence and becomes the middle part, where the network manages the transfer data of the aircraft delivered via the OpenFlow (OF) switch. To this end, the controller provides an interface for managing, controlling, and managing this switch flow table. Run tests to calculate controller throughput and latency levels and test using the cbance tool, which can test transmission control protocol (TCP) and user datagram protocol (UDP) protocols. The tests are run by forcing the controller to run at maximum without any additional settings (default settings) in order to use the correct information about the controller’s capabilities. Because of this need, you need to test the performance of your controller. In this study, the tests were run on three popular controllers. Test results show that flowed controllers are more stable than open network operating dystem (ONOS) and open daylight (ODL) controllers in managing switch and host loads.]]>
<![CDATA[Hen maternal care inspired optimization framework for attack detection in wireless smart grid network ]]>
<![CDATA[Ganesamoorthy, Narmadha]]>;
<![CDATA[Sakthivel, B.]]>;
<![CDATA[Subbramania, Deivasigamani]]>;
<![CDATA[Balasubadra, K.]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 1: April 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v13i1.pp123-130
<![CDATA[In the power grid, communication networks play an important role in exchanging smart grid-based information. In contrast to wired communication, wireless communication offers many benefits in terms of easy setup connections and low-cost high-speed links. Conversely, wireless communications are commonly more vulnerable to security threats than wired ones. All power equipment devices and appliances in the smart distribution grid (SDG) are communicated through wireless networks only. Most security research focuses on keeping the SDG network from different types of attacks. The denial-of-service (DoS) attack is consuming more energy in the network leads to a permanent breakdown of memory. This work proposes a new metaheuristic optimization inspired by maternal care of hen to their children called hen maternal care (HMCO) inspired optimization. The HMCO algorithm mimics the care shown by hen for their children in nature. The mother hen is always watchful and protects its chicks against predators. All chickens utilize different calls to designate flying predators like falcons and owls from ground seekers like foxes and coyotes, showing that they can both survey a danger and advise different chickens how to set themselves up. Our method shows greater performance among other standard algorithms.]]>
<![CDATA[Novel DV-hop algorithm-based machines learning technics for node localization in rang-free wireless sensor networks ]]>
<![CDATA[Oumaima Liouane]]>;
<![CDATA[Smain Femmam]]>;
<![CDATA[Toufik Bakir]]>;
<![CDATA[Abdessalem Ben Abdelali]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 12, No 2: August 2023 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v12i2.pp140-149
<![CDATA[Localization is a critical concern in many wireless sensor network (WSN) applications. Furthermore, correct information regarding the geographic placements of nodes (sensors) is critical for making the collected data valuable and relevant. Because of their benefits, such as simplicity and acceptable accuracy, the based connectivity algorithms attempt to localize multi-hop WSN. However, due to environmental factors, the precision of localisation may be rather low. This publication describes an Extreme Learning Machine (ELM) technique for minimizing localization error in range-free WSN. In this paper, we propose a Cascade Extreme Learning Machine (Cascade-ELM) to increase localization accuracy in Range-Free WSNs. We tested the proposed approaches in a variety of multi-hop WSN scenarios. Our research focused on an isotropic and irregular environment. The simulation results show that the proposed Cascade-ELM algorithm considerably improves localization accuracy when compared to previous algorithms derived from smart computing approaches. When compared to previous work, isotropic environments show improved localization results.]]>
<![CDATA[Predicting anomalies in computer networks using autoencoder-based representation learning ]]>
<![CDATA[Khan, Shehram Sikander]]>;
<![CDATA[Mailewa, Akalanka Bandara]]>
<![CDATA[ International Journal of Informatics and Communication Technology (IJ-ICT) Vol 13, No 1: April 2024 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/ijict.v13i1.pp9-26
<![CDATA[Recent improvements in the internet of things (IoT), cloud services, and network data variety have increased the demand for complex anomaly detection algorithms in network intrusion detection systems (IDSs) capable of dealing with sophisticated network threats. Academics are interested in deep and machine learning (ML) breakthroughs because they have the potential to address complex challenges such as zero-day attacks. In comparison to firewalls, IDS are the initial line of network security. This study suggests merging supervised and unsupervised learning in identification systems IDS. Support vector machine (SVM) is an anomaly-based classification classifier. Deep autoencoder (DAE) lowers dimensionality. DAE are compared to principal component analysis (PCA) in this study, and hyper-parameters for F-1 micro score and balanced accuracy are specified. We have an uneven set of data classes. precision-recall curves, average precision (AP) score, train-test times, t-SNE, grid search, and L1/L2 regularization methods are used. KDDTrain+ and KDDTest+ datasets will be used in our model. For classification and performance, the DAE+SVM neural network technique is successful. Autoencoders outperformed linear PCA in terms of capturing valuable input attributes using t-SNE to embed high dimensional inputs on a two-dimensional plane. Our neural system outperforms solo SVM and PCA encoded SVM in multi-class scenarios.]]>
