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.
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<![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[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[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.]]>
<![CDATA[Remote practical instruction using web browsers ]]>
<![CDATA[Kentarou, Nagaki]]>;
<![CDATA[Satoshi, Fujita]]>
<![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.pp57-66
<![CDATA[This paper introduces a novel approach to remote coaching, specifically targeting the body movements of learners participating remotely. The proposed system employs a smartphone camera to capture the learner’s body and represent it as a 3D avatar. The instructor can then offer guidance and instruction by manipulating the 3D avatar’s shape, which is displayed on a web browser. The main challenge faced by the system is to enable the sharing and editing of 3D objects among users. Since the HTML5 drag-and-drop feature is inadequate for transforming virtual objects consisting of multiple interconnected rigid bodies, the system tracks the pivot point of the manipulated rigid body. It assigns attributes such as pivot points and action points to each object, extending beyond their 2D screen coordinates. To implement the system, an interactive web application framework following the model-view-view-model (MVVM) architecture is utilized, incorporating Vue.js, Three.js, and Google Firebase. The prototype system takes advantage of the data binding capability of the framework and successfully operates within the 3D space of a web browser. Experimental results demonstrate that it can effectively share transformation information with an average delay of 300 ms.]]>
<![CDATA[Adaptive resource allocation in NOMA-enabled backscatter communications systems ]]>
<![CDATA[Das, Deepa]]>;
<![CDATA[Khadanga, Rajendra Kumar]]>;
<![CDATA[Rout, Deepak Kumar]]>
<![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.pp67-79
<![CDATA[The integration of NOMA with Backscatter communication (BackCom) is a promising solution for developing a green future wireless network. However, system performance degrades with the deployment of multiple backscatter devices (BDs) in a network. Hence, energy efficiency (EE) maximization with proper resource allocation is among the primary concerns. In this regard, this paper proposes an adaptive resource allocation method for maximizing EE by simultaneously optimizing the transmission power from the base station (BS), power allocation coefficients, and reflection coefficients under the constraints of maximum allowable transmission power and minimum achievable data rate. Specifically, an iterative method based on a parametric transformation approach is adopted for maximizing EE by jointly optimizing the coefficients, in which the power allocation problem to the BDs is solved by an adaptive method that is based on improved proportionate normalized least mean square (IPNLMS) algorithm. Then, the system performance is evaluated, and the impact of different parameters is also studied it is observed that EE is significantly improved as compared to the existing scheme, and maximum at η=-0.5.]]>
<![CDATA[A micro size terahertz wheel shaped antenna with non-defected ground structure ]]>
<![CDATA[Swaminathan, Narayanan]]>;
<![CDATA[Rajendiran, Murugesan]]>
<![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.pp101-107
<![CDATA[A micro dimension antenna with wheel-geometrical wide-band terahertz (THz) is suggested in this research. In the circular shaped patch, concentric circle shaped slots are incorporated to form a wheel shaped patch antenna. The suggested model is designed on a polymide substrate with dielectric constant of 4.3 and thickness of 20 µm. The suggested prototype antenna is very much compact in size of 210×160 µm2 . The designed antenna achieves a wideband operation from 8.692 THz to 9.772 THz. This prototype antenna’s maximum realized gain is 10.2 dBi at 9.0 THz. This high gain is important for wide range of wireless applications. The radiation pattern, radiation efficiency, reflection coefficient, surface current distribution and voltage standing wave ratio are examined through the simulation results. In future video rate imaging system, super fast close-range in-door wireless communication, biomedical picturing, homeland defence equipments, security scanning, explosive detection, and characterisation of materials in the THz level will be benefited from the suggested THz antenna.]]>
<![CDATA[Indonesian generative chatbot model for student services using GPT ]]>
<![CDATA[Priccilia, Shania]]>;
<![CDATA[Girsang, Abba Suganda]]>
<![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.pp50-56
<![CDATA[The accessibility of academic information greatly impacts the satisfaction and loyalty of university students. However, limited university resources often hinder students from conveniently accessing information services. To address this challenge, this research proposes the digitization of the question-answering process between students and student service staff through the implementation of generative chatbot. A generative chatbot can provide students with human-like responses to academic inquiries at their convenience. This research developed generative chatbot using pre-trained GPT-2 architecture in three different sizes, specifically designed for addressing practicum-related questions in a private university in Indonesia. The experiment utilized 1288 question-answer pairs in Indonesian and demonstrated the best performance with a BLEU score of 0.753, signifying good performance accuracy in generating text despite dataset limitations.]]>
<![CDATA[ChatGPT's effect on the job market: how automation affects employment in sectors using ChatGPT for customer service ]]>
<![CDATA[Mishra, Debani Prasad]]>;
<![CDATA[Agarwal, Nandini]]>;
<![CDATA[Shah, Dhruvi]]>;
<![CDATA[Salkuti, Surender Reddy]]>
<![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.pp116-122
<![CDATA[A significant language model called ChatGPT, created by OpenAI, has gained attention in artificial intelligence (AI) and natural language processing. This research paper aims to provide an in-depth analysis of ChatGPT and its potential impact on the future, including its limitations, pros and cons, and how it came to be. This paper first provides a brief overview of ChatGPT, including its architecture and training process, and how it differs from previous language models. It then delves into the model's limitations, such as its lack of common sense and susceptibility to discrimination or biases present in the data it was trained on. This paper also explores the potential benefits of ChatGPT, such as its ability to generate human-like text, its potential use in customer service, and its potential impact on the job market. The paper also discusses the ethical and social implications of ChatGPT, such as the potential for the model to perpetuate biases and the need for transparency and accountability in its deployment. Finally, the paper concludes by discussing the future of ChatGPT and similar language models and their potential impact on various industries and society as a whole. Overall, this research paper provides a comprehensive and nuanced survey of the AI tool ChatGPT and its potential impact on the future.]]>
<![CDATA[Traffic accident classification using IndoBERT ]]>
<![CDATA[Naufal, Muhammad Alwan]]>;
<![CDATA[Girsang, Abba Suganda]]>
<![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.pp42-49
<![CDATA[Traffic accidents are a widespread concern globally, causing loss of life, injuries, and economic burdens. Efficiently classifying accident types is crucial for effective accident management and prevention. This study proposes a practical approach for traffic accident classification using IndoBERT, a language model specifically trained for Indonesian. The classification task involves sorting accidents into four classes: car accidents, motorcycle accidents, bus accidents, and others. The proposed model achieves a 94% accuracy in categorizing these accidents. To assess its performance, we compared IndoBERT with traditional methods, random forest (RF) and support vector machine (SVM), which achieved accuracy scores of 85% and 87%, respectively. The IndoBERT-based model demonstrates its effectiveness in handling the complexities of the Indonesian language, providing a useful tool for traffic accident classification and contributing to improved accident management and prevention strategies.]]>
<![CDATA[A custom-built deep learning approach for text extraction from identity card images ]]>
<![CDATA[Suddul, Geerish]]>;
<![CDATA[Seguin, Jean Fabrice Laurent]]>
<![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.pp34-41
<![CDATA[Information found on an identity card is needed for different essential tasks and manually extracting this information is time consuming, resource exhaustive and may be prone to human error. In this study, an optical character recognition (OCR) approach using deep learning techniques is proposed to automatically extract text related information from the image of an identity card in view of developing an automated client onboarding system. The OCR problem is divided into two main parts. Firstly, a custom-built image segmentation model, based on the U-net architecture, is used to detect the location of the text to be extracted. Secondly, using the location of the identified text fields, a (CRNN) based on long short-term memory (LSTM) cells is trained to recognise the characters and build words. Experimental results, based on the national identity card of the Republic of Mauritius, demonstrate that our approach achieves higher accuracy compared to other studies. Our text detection module has an intersection over union (IOU) measure of 0.70 with a pixel accuracy of 98% for text detection and the text recognition module achieved a mean word recognition accuracy of around 97% on main fields of the identity card.]]>
