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Symmetrical cryptographic algorithms in the lightweight internet of things

10.11591/ijict.v14i1.pp307-314
Akshaya Dhingra , Vikas Sindhu , Anil Sangwan
The internet of things (IoT) has emerged as a prominent area of scrutiny. It is being deployed in multiple applications like smart homes, smart agriculture, intelligent surveillance systems, and even innovative industries. Security is a significant issue that needs to be addressed in these types of networks. This paper aims to describe symmetrical lightweight cryptographic algorithms (SLCAs) for lightweight IoT networks. The article focuses on discussing the principal difficulties of using cryptography in lightweight IoT devices, exploring SLCAs and their types based on structure formation throughout the literature survey, and comparing and evaluating different LCAs proposed in recent research. The main goal is to demonstrate how to solve the issues associated with conventional cryptography techniques and how lightweight cryptography algorithms aid limited IoT devices in achieving cybersecurity objectives.
Volume: 14
Issue: 1
Page: 307-314
Publish at: 2025-04-01

The 360° beach video: a supporting mindfulness intervention with virtual reality

10.11591/ijict.v14i1.pp250-258
Rohmatus Naini , Mungin Eddy Wibowo , Edy Purwanto , Mulawarman Mulawarman , E. Oos M. Anwas
This article describes optimizing virtual reality (VR) with a 360° beach video model used for mindfulness interventions. Using VR with 360° beach videos to support the presence of an immersive environment can effectively support mindfulness practices. Students are interested in the integration of technology in school counseling. VR helps in creating immersive environments such as forests, beaches, waterfalls, etc. so that students focus more on practicing mindfulness and attention in the current moment. This article focuses on optimizing 360° beach videos in the breathing mindfulness process so that it helps bring out real experiences. Obstacles to practicing mindfulness include lack of focus, mind wandering and not concentrating. through the use of 360° beach videos with VR can increase focus and be more effective in mindfulness practice.
Volume: 14
Issue: 1
Page: 250-258
Publish at: 2025-04-01

A hybrid approach of pattern recognition to detect marine animals

10.11591/ijict.v14i1.pp240-249
Vijayalakshmi Balachandran , Thanga Ramya Shanmugavel , Ramar Kadarkarayandi , Vijayalakshmi Kandhasamy
Acquiring up-to-date knowledge about various animals will have a significant impact on effectively managing species within the ecosystem. Manually identifying animals and their traits continues to be a costly and time-consuming process. The development of a system using the most recent developments in computer vision machine learning was necessary to address the issues of detecting sharks and aquatic species in areas filled with surfers, rocks, and various other potential false positives. In the ocean most of the species are cold-blooded animals hence they cannot be tracked with thermal cameras. Ocean’s dynamic environment affects simple techniques like color separation, intensity histograms, and optical flow. Hence a hybrid approach using convolutional neural network - support vector machine (CNN-SVM) classifier is proposed to perform the pattern recognition. A CNN is employed for feature extraction by using the histogram of gradients value. Subsequently, a SVM classifier is employed to identify and categorise marine species in the vicinity of the seacoast. This serves to notify individuals who engage in swimming activities in the ocean. The suggested model is evaluated against alternative machine learning approaches, and it achieves a superior accuracy of 95% compared to the others.
Volume: 14
Issue: 1
Page: 240-249
Publish at: 2025-04-01

Teaching learning based optimization algorithm for effective analysis of power quality using dynamic voltage restorer

10.11591/ijict.v14i1.pp268-275
Soumya Ranjan Das , Surender Reddy Salkuti
In this study, the load voltage is dynamically restored utilising the dynamic voltage restorer (DVR) using the voltage injection approach. The injected voltage is generated using a voltage-source inverter (VSI), which is necessary to correct for the utility network's sag and swell characteristics voltage. The restoration process is dependent on the condition and quality of the utility system, i.e., it injects energy into the external system for the duration of voltage sag, and during voltage swell, energy is absorbed by the compensator from the external system, causing an rise in dc link voltage, which is connected across the VSI. In this study two different controllers are employed based on a learning based optimized algorithm. The simulation results are shown using two different controllers and the performance of the proposed controller is found to be a better one.
Volume: 14
Issue: 1
Page: 268-275
Publish at: 2025-04-01

Quality and shelf-life prediction of cauliflower using machine learning under vacuum and modified atmosphere packaging

10.11591/ijai.v14.i2.pp907-916
Md. Apu Hosen , Dr. Syed Md. Galib
Ensuring the freshness and quality of cauliflower during storage and transportation is essential due to its high perishability. This study harnesses the power of machine learning to predict the quality and shelf-life of cauliflower under cost-effective vacuum and modified atmosphere packaging (MAP) techniques. By investigating key parameters such as total soluble solids (TSS), pH, weight loss, and color change, a significant impact on post-packaging quality was identified. To address the challenge of accurate color change measurement, an innovative method utilizing a bilateral filter for noise reduction and particle swarm optimization (PSO) with Markov random field (MRF) segmentation was developed. TSS, weight loss, and color change were identified as key parameters, and leveraging these parameters, artificial neural networks (ANN) were employed to create highly precise predictive models, achieving R-squared values of 0.952 for TSS, 0.992 for weight loss, and 0.981 for color change. This approach not only enhances the efficiency and sustainability of food production and distribution but also minimizes food waste and maximizes profitability for cauliflower in global markets through the use of cost-effective packaging solutions.
Volume: 14
Issue: 2
Page: 907-916
Publish at: 2025-04-01

Digital learning and student outcomes: a mathematical synthesis from the last decade

10.11591/ijere.v14i2.30431
Aizhan Koishybekova , Nazym Zhanatbekova , Yerlan Khaimuldanov , Assel Orazbayeva , Dariya Abykenova
Understanding the broad effects of e-learning on educational outcomes and the contributing factors is crucial, especially given the conflicting conclusions from past research. This is important to ensure that educators and policymakers do not waste resources and focus effectively when prioritizing digital investments. Hence, this study sought to provide a comprehensive quantitative review of the extant evidence on how digital learning initiatives affect student outcomes within the cognitive domain across different subjects and educational levels. To that end, a meta-analysis was performed encompassing 17 studies spanning from 2015 to 2023, involving 1,896 participants. The quantitative synthesis was completed using a random-effects model. The results indicate a positive small to medium overall effect size (Hedge’s g=.49, adjusted for publication bias) for technology-assisted interventions compared to traditional education. Subgroup analyses revealed nuances, such as higher academic gains associated with active cognitive engagement modes and potential disparities between school and higher education settings. However, no factors significantly affected the pooled effect sizes for cognitive outcomes. Nevertheless, considerable between-study heterogeneity could compromise the estimates. The meta-analysis underscores the scarcity of rigorous studies in the digital learning domain. Further research directions are outlined.
Volume: 14
Issue: 2
Page: 1150-1161
Publish at: 2025-04-01

Assessing the user experience of marker-based 3D WebAR applications using user experience questionnaire

10.11591/ijict.v14i1.pp31-41
Nooralisa Mohd Tuah , Wan Nooraishya Wan Ahmad , Ryan MacDonell Andrias , Dg. Senandong Ajor , Suaini Sura , Ahmad Rizal Ahmad Rodzuan
Marker-based 3D web-based augmented reality (WebAR) applications are an emerging field that merges web technologies with augmented reality. WebAR has gained popularity because of its ability to provide users with a reliable and autonomous platform. Yet, a limited investigation has verified its application and user perspective on its ability to function. This study is designed to evaluate the user experiences of marker-based 3D WebAR applications using the user experience questionnaire (UEQ). This study assesses various elements of the user experience, including attractiveness, clarity, engagement, efficiency, and innovation, utilizing the UEQ. This study aims to analyze user perceptions and interaction patterns thoroughly to get useful insights into the usability and user satisfaction aspects of marker-based 3D WebAR apps. The findings reveal that the WebAR app is both appealing and efficient, instilling confidence in its users. This underscores the pivotal role of user experience in shaping the effectiveness and reception of WebAR applications. This research has the potential to influence the creation of more user-focused and engaging marker-based 3D WebAR experiences, improving user engagement and immersion in web-based augmented reality environments.
Volume: 14
Issue: 1
Page: 31-41
Publish at: 2025-04-01

Planar hexagonal patch multiple input multiple output 4x4 antenna for UWB applications

10.11591/ijict.v14i1.pp174-181
Nasrul Nasrul , Firdaus Firdaus , Nurraudya Tuz Zahra , Maulidya Rachmawati
The combination of Multiple Input Multiple Output (MIMO) antennas and Ultra-Wideband (UWB) technology offers several advantages, including reduced interference, improved isolation, and optimized dual paths. These benefits extend the range and enhance signal quality. However, designing UWB-MIMO antennas presents challenges, such as achieving low mutual coupling for high isolation and creating small-sized antennas suitable for portable devices while being effective for UWB frequencies in a MIMO configuration. The proposed antenna is a 4x4 planar MIMO antenna with a hexagon-shaped patch, a partial ground plane featuring an inverted L-stub on the left side, and a plus-shaped slot in the centre ground. It has dimensions of 32 x 32 x 1.6 mm³ and is capable of achieving a wide bandwidth of 3-12.5 GHz. The antenna's performance measurements are impressive: return loss below -10 dB at frequencies of 3-12.5 GHz, mutual coupling below -16.5 dB, Envelope Correlation Coefficient (ECC) bellow 0.005, Diversity gain of more than 9.97, Total Active Reflection Coefficient (TARC) below -10 dB. Based on these results, the proposed antenna offers excellent performance for UWB applications, featuring high efficiency, minimal interference between antenna elements, and optimal diversity performance.
Volume: 14
Issue: 1
Page: 174-181
Publish at: 2025-04-01

Finite state machine for retro arcade fighting game development

10.11591/ijict.v14i1.pp102-110
Muhammad Bambang Firdaus , Alan Zulfikar Waksito , Andi Tejawati , Medi Taruk , M. Khairul Anam , Akhmad Irsyad
Traditional fighting games are a competitive genre where players engage in one-on-one combat, aiming to reduce their opponent's health points to zero. These games often utilize two-dimensional (2D) graphics, enabling players to execute various character movements such as punching, jumping, and crouching. This research investigates the effectiveness of the finite state machine (FSM) method in developing a combo system for a retro fighting game, focusing on its implementation within the Godot Engine. The FSM method, which structures game behavior through states, events, and actions, is central to the game's control system. By employing the game development life cycle (GDLC) methodology, this study ensures a systematic and structured approach to game design. Special attention is given to the regulation of the combo hit system for the game's protagonist in Brawl Tale. The research culminates in the successful development of the retro fighting game Brawl Tale, demonstrating that the FSM method significantly enhances the fluidity and responsiveness of character movements. The findings suggest that the FSM method is an effective tool for simplifying and improving gameplay mechanics in retro-style fighting games.
Volume: 14
Issue: 1
Page: 102-110
Publish at: 2025-04-01

The integration of discrete contourlet transform in OFDM framework for future wireless communication

10.11591/ijict.v14i1.pp182-194
Mohamed Hussien Mohamed Nerma , Adam Mohamed Ahmed Abdo
In the upcoming era, the forthcoming sixth-generation (6G) wireless communication network will demand highly efficient technology to support extensive capacity, ultra-high speeds, low latency, scalability, and adaptability. While the current fifth-generation (5G) wireless communication system relies on OFDM technology, the evolution towards a beyond 5G wireless communication system necessitates a new OFDM framework. This study introduces a novel OFDM system that integrates the discrete Contourlet transform. A comparative analysis has been conducted among the proposed system, conventional OFDM, and curvelet-based OFDM systems. The results indicate that the proposed system offers improvements in bit error rate (BER), reduced computational complexity, decreased peak-to-average power ratio (PAPR), and enhanced power spectrum density (PSD) when contrasted with both the traditional and curvelet-based systems.
Volume: 14
Issue: 1
Page: 182-194
Publish at: 2025-04-01

Graph-based methods for transaction databases: a comparative study

10.11591/ijai.v14.i2.pp1663-1672
Wael Ahmad AlZoubi , Ibrahim Mahmoud Alturani , Roba Mahmoud Ali Aloglah
There has been an increased demand for structured data mining. Graphs are among the most extensively researched data structures in discrete mathematics and computer science. Thus, it should come as no surprise that graph-based data mining has gained popularity in recent years. Graph-based methods for a transaction database are necessary to transform all the information into a graph form to conveniently extract more valuable information to improve the decision-making process. Graph-based data mining can reveal and measure process insights in a detailed structural comparison strategy that is ready for further analysis without the loss of significant details. This paper analyzes the similarities and differences among four of the most popular graph-based methods that is applied to mine rules from transaction databases by abstracting them out as a concrete high-level interface and connecting them into a common space.
Volume: 14
Issue: 2
Page: 1663-1672
Publish at: 2025-04-01

An model for structured the NoSQL databases based on machine learning classifiers

10.11591/ijict.v14i1.pp229-239
Amine Benmakhlouf
Today, the majority of data generated and processed in organizations is unstructured. NoSQL database management systems perform the management of this data. The problem is that these unstructured databases cannot be analyzed by traditional OLAP analytical treatments. The latter are mainly used in structured relational databases. In order to apply OLAP analyses on NoSQL data, the structuring of this data is essential. In this paper, we propose a model for structuring the data of a document-oriented NoSQL database using machine learning (ML). This method is broken down into three steps, first the vectorization of documents, then the learning via different ML algorithms and finally the classification, which guarantees that documents with the same structure will belong to the same collection. Therefore, the modeling of a data warehouse can be carried out in order to create OLAP cubes. Since the models found by learning allow the parallel computation of the classifier, our approach represents an advantage in terms of speed since we will avoid doubly iterative algorithms, which rely on textual comparisons (TC). A comparative study of the performances is carried out in this work in order to detect the most efficient methods to perform this type of classification.
Volume: 14
Issue: 1
Page: 229-239
Publish at: 2025-04-01

Evaluating ChatGPT’s Mandarin “yue” pronunciation system in language learning

10.11591/ijai.v14.i2.pp1634-1641
Yoke Lian Lau , Swee Mee Tan , Anna Lynn Abu Bakar , Zi Xian Yong , Zi Hong Yong , Ernahwatikah Nasir , Chen Jung Ku
By incorporating voice control technology into ChatGPT, it becomes possible to engage in conversations or dialogues with individuals who are actively engaged in the process of acquiring language skills. Our study team conducted a modest experiment to evaluate the efficacy of a voice control feedback system in facilitating the mastery of the most challenging pronunciation of the Mandarin syllable "yue". The objective of this study is to evaluate the effectiveness of voice-controlled ChatGPT in aiding learners to acquire accurate pronunciation of the Mandarin phoneme "yue". Furthermore, the study seeks to investigate the methods utilised by the ChatGPT model in identifying and distinguishing the word "yue" when it is used alone or in combination with "ye" and "yi". We employed many testing approaches, including single-word instances, paired instances, and the integration of phrases. In addition, we evaluated the model's ability to accurately detect the term "yue" in short sentences and, ultimately, in a longer sentence.
Volume: 14
Issue: 2
Page: 1634-1641
Publish at: 2025-04-01

Data analysis and visualization on titanic and student’s performance datasets-an exploratory study

10.11591/ijict.v14i1.pp68-76
Seong-Cheol Kim , Surender Reddy Salkuti , Alka Manvayalar Suresh , Madhu Sree Sankaran
Exploratory data analysis (EDA) is all about exploring the data in order to identify any underlying pattern before you try to use it to make a predictive model. It also plays a major role in the data discovery process as it is used to analyze data and to recapitulate their different characteristics, which is displayed efficiently with the help of data visualization methods. This paper aims to identify errors in the dataset, to understand the existing hidden structure and to identify new ones, to detect points in a dataset that deviate to a greater extent from the collected data (outliers), and also to find any relationship or intersection between the variables and constants. Two datasets are used namely ‘Titanic’ and ‘student’s performance’ to perform data analysis and ‘data visualization’ to depict ‘exploratory data analysis’ which acts as an important set of tools for recognizing a qualitative understanding. The datasets were explored and hence it assisted with identifying patterns, outliers, corrupt data, and discovering the relationship between the fields in the dataset.
Volume: 14
Issue: 1
Page: 68-76
Publish at: 2025-04-01

Explainable zero-shot learning and transfer learning for real time Indian healthcare

10.11591/ijict.v14i1.pp91-101
Swati Saigaonkar , Vaibhav Narawade
Clinical note research is globally recognized, but work on real-time data, particularly from India, is still lagging. This study initiated by training models on medical information mart for intensive care (MIMIC) clinical notes, focusing on conditions like chronic kidney disease (CKD), myocardial infarction (MI), and asthma using the structured medical domain bidirectional encoder representations from transformers (SMDBERT) model. Subsequently, these models were applied to an Indian dataset obtained from two hospitals. The key difference between publicly available datasets and real-time data lies in the prevalence of certain diseases. For example, in a real-time setting, tuberculosis may exist, but the MIMIC dataset lacks corresponding clinical notes. Thus, an innovative approach was developed by combining a fine-tuned SMDBERT model with a customized zero-shot learning method to effectively analyze tuberculosis-related clinical notes. Another research gap is the lack of explainability because deep learning (DL) models are inherently black-box. To further strengthen the reliability of the models, local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) explanations were projected along with narrative explanations which generated explanations in a natural language format. Thus, the research provides a significant contribution with ensemble technique of zero-shot learning and SMDBERT model with an accuracy of 0.92 as against the specialized models like scientific BERT (SCIBERT), biomedical BERT (BIOBERT) and clinical BioBERT.
Volume: 14
Issue: 1
Page: 91-101
Publish at: 2025-04-01
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