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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

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

Learning high-level spectral-spatial features for hyperspectral image classification with insufficient labeled samples

10.11591/ijai.v14.i2.pp1211-1219
Douglas Omwenga Nyabuga , Godfrey Nyariki
Hyperspectral image (HSI) classification research is a hot area, with a mass of new methods being developed to improve performance for specific applications that use spatial and spectral image material. However, the main obstacle for scientists is determining how to identify HSIs effectively. These obstacles include an increased presence of redundant spectral information, high dimensionality in observed data, and limited spatial features in a classification model. To this end, we, therefore, proposed a novel approach for learning high-level spectral-spatial features for HSI classification with insufficient labeled samples. First, we implemented the principal component analysis (PCA) technique to reduce the high dimensionalities experienced. Second, a fusion of 2D and 3D convolutions and DenseNet, a transfer learning network for feature learning of both spatial-spectral pixels. The achieved experimental results are comparatively satisfactory to contrasted approaches on the widely used HSI images, i.e., the University of Pavia and Indian Pines, with an overall classification accuracy of 97.80% and 97.60%, respectively.
Volume: 14
Issue: 2
Page: 1211-1219
Publish at: 2025-04-01

Event detection in soccer matches through audio classification using transfer learning

10.11591/ijai.v14.i2.pp1441-1449
Bijal Utsav Gadhia , Shahid S. Modasiya
Addressing the complexities of generating sports summaries through machine learning, our research aims to bridge the gap in audio-based event detection, particularly in soccer games. We introduce an extended ResNet-50 deep learning approach for soccer audio, emphasizing key moments from large soccer content archives through the use of transfer learning. The proposed model accurately classifies soccer audio segments into two categories: i) events, representing crucial in-game occurrences and ii) no events, denoting less impactful moments. The model involves complete audio preprocessing, the implementation of proposed model using transfer learning and the classification of events. The model’s reliability is validated using the dataset soccer action dataset compilation (SADC), involves dataset creation by football fans. Comparative analysis with pre-trained models such as VGG19, DesNet121, and EfficientNetB7 demonstrates the superior performance of the extended ResNet-50 based approach. Results across different epochs reveal consistently high accuracy, precision, recall, and F1-score, emphasizing the proposed model's effectiveness in event detection through audio classification. The paper concludes that the proposed model offers a robust solution for detecting an event from audio of soccer sports providing valuable insights for fans, analysts, and content creators to identify interested moments from soccer game with low failure.
Volume: 14
Issue: 2
Page: 1441-1449
Publish at: 2025-04-01

Enhanced hippopotamus optimization algorithm for power system stabilizers

10.11591/ijeecs.v38.i1.pp22-31
Widi Aribowo , Toufik Mzili , Aliyu Sabo
This article presents techniques for modifying the power system stabilizer's (PSS) parameters. An enhanced version of the hippocampal optimization algorithm (HO) is presented here. HO represents a novel approach in metaheuristic methodology, having been inspired by the observed clinging behavior in hippos. The notion of the HO is defined using a trinary-phase model that includes their position updates in rivers or ponds, defensive techniques against predators, and mathematically described evasive methods. To confirm the efficacy of the recommended approach, this article provides comparison simulations of the PSS objective function and transient response. This study employs validation through a comparison between Original HO and conventional methods. Simulation results demonstrate that, when compared to competing algorithms, the suggested approach yields optimal results and, in some cases, exhibits fast convergence. It is known that, in comparison to the original HO approach, the recommended way can lower the average undershoot of the rotor angel and speed by 12.049% and 26.97%, respectively.
Volume: 38
Issue: 1
Page: 22-31
Publish at: 2025-04-01

Interactive communication human-robot interface for reduced mobility people assistance

10.11591/ijai.v14.i2.pp917-924
Robinson Jiménez Moreno , Anny Astrid Espitia Cubillos , Esperanza Rodríguez Carmona
Communication between a robot and its user is essential for the execution of tasks, even more so in a scenario where the robot is designed to assist people with reduced mobility. This document presents the evaluation of a conversation script between a human user and a robot for assistance using pre-recorded responses, for this a methodology with three phases was proposed and applied: establishment of the training scheme of a convolutional network that allows recognize user's words for execution of tasks by the robot, generation of dialogue between the user and possible interactions with the assistive robot and finally, the measurement of perception of interface users. Results show a high level of accuracy with words selected to command the robot, using a convolutional neural network, with an audio input discriminated in its components mel frequency cepstral coefficients (MFCCs) and command sets of male and female voices. It was possible to establish a dialogue model with three scenes to recognize the residential environment, rename spaces and execute action commands to move elements. It is concluded the designed instrument is reliable and the perception of proposed interactive communication interface is good in terms of usability (effectiveness, efficiency, and user satisfaction).
Volume: 14
Issue: 2
Page: 917-924
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

Machine learning based stator-winding fault severity detection in induction motors

10.11591/ijeecs.v38.i1.pp182-192
Partha Mishra , Shubhasish Sarkar , Sandip Saha Chowdhury , Santanu Das
Approximately 35% of all induction motor defects are caused by stator inter-turn faults. In this paper a novel algorithm has been proposed to analyze the three-phase stator current signals captured from the motor while it is in operation. The suggested method seeks to identify stator inter-turn short circuit faults in early stage and take the appropriate action to prevent the motor's condition from getting worse. Three-phase current signals have been captured under healthy and faulty conditions of the motor. Involving discrete wavelet transform (DWT) based decomposition followed by reconstruction using inverse DWT (IDWT), 50 Hz fundamental component has been removed from the captured raw current signals. Subsequently, from each phase current 15 statistical parameters have been retrieved. The statistical parameters include mean, standard deviation, skewness, kurtosis, peak-to-peak, root mean square (RMS), energy, crest factor, form factor, impulse factor, and margin factor. At the end, a standard machine learning algorithm namely error correcting output codes-support vector machine (ECOC-SVM) has been employed to classify six different severity of stator winding faults. The proposed fault diagnosis method is load and motor-rating independent.
Volume: 38
Issue: 1
Page: 182-192
Publish at: 2025-04-01

Innovating household efficiency: the internet of things intelligent drying rack system

10.11591/ijeecs.v38.i1.pp99-106
Norhalida Othman , Zakiah Mohd Yusoff , Mohamad Fadzli Khamis @ Subari , Nur Amalina Muhamad , Noor Hafizah Khairul Anuar
The intelligent drying rack system (IIDRS) proposes an innovative approach to modernize clothes drying practices using internet of things (IoT) technology. Combining an Arduino Uno microcontroller, ESP8266 for data transmission, and an array of sensors including limit switches, light dependent resistors (LDRs), rain sensors, and temperature/humidity sensors, the IIDRS enables automated control of the drying rack and fan. Its remote accessibility via Blynk apps allows users to conveniently adjust settings and monitor drying progress. By autonomously adjusting drying cycles based on real-time environmental conditions, the IIDRS enhances efficiency and minimizes inconveniences such as wet clothes during rainfall. Moreover, it contributes to sustainable living by optimizing energy consumption through weather-based operation. With its intuitive interface and compatibility with modern lifestyles, the IIDRS represents a significant advancement in smart home solutions, showcasing the transformative potential of IoT technologies in everyday tasks.
Volume: 38
Issue: 1
Page: 99-106
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

SMOTE tree-based autoencoder multi-stage detection for man-in-the-middle in SCADA

10.11591/ijeecs.v38.i1.pp133-144
Freska Rolansa , Jazi Eko Istiyanto , Afiahayati Afiahayati , Aufaclav Zatu Kusuma Frisky
Security incidents targeting supervisory control and data acquisition (SCADA) infrastructure are increasing, which can lead to disasters such as pipeline fires or even lost of lives. Man-in-the-middle (MITM) attacks represent a significant threat to the security and reliability of SCADA. Detecting MITM attacks on the Modbus SCADA networks is the objective of this work. In addition, this work introduces SMOTE tree-based autoencoder multi-stage detection (STAM) using the Electra dataset. This work proposes a four-stage approach involving data preprocessing, data balancing, an autoencoder, and tree classification for anomaly detection and multi-class classification. In terms of attack identification, the proposed model performs with highest precision, detection rate/recall, and F1 score. In particular, the model achieves an F1 score of 100% for anomaly detection and an F1 score of 99.37% for multi-class classification, which is preeminence to other models. Moreover, the enhanced performance of multi-class classification with STAM on minority attack classes (replay and read) has shown similar characteristics in features and a reduced number of misclassifications in these classes.
Volume: 38
Issue: 1
Page: 133-144
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

Hybrid model detection and classification of lung cancer

10.11591/ijai.v14.i2.pp1496-1506
Rami Yousuf , Eman Yaser Daraghmi
Lung cancer ranks among the most prevalent malignancies worldwide. Early detection is pivotal to improving treatment outcomes for various cancer types. The integration of artificial intelligence (AI) into image processing, coupled with the availability of comprehensive historical lung cancer datasets, provides the chance to create a classification model based on deep learning, thus improving the precision and effectiveness of detecting lung cancer. This not only aids laboratory teams but also contributes to reducing the time to diagnosis and associated costs. Consequently, early detection serves to conserve resources and, more significantly, human lives. This study proposes convolutional neural network (CNN) models and transfer learning-based architectures, including ResNet50, VGG19, DenseNet169, and InceptionV3, for lung cancer classification. An ensemble approach is used to enhance overall cancer detection performance. The proposed ensemble model, composed of five effective models, achieves an F1-score of 97.77% and an accuracy rate of 97.5% on the IQ-OTH/NCCD test dataset. These findings highlight the effectiveness and dependability of our novel model in automating the classification of lung cancer, outperforming prior research efforts, streamlining diagnosis processes, and ultimately contributing to the preservation of patients' lives.
Volume: 14
Issue: 2
Page: 1496-1506
Publish at: 2025-04-01

Enhancing predictive modelling and interpretability in heart failure prediction: a SHAP-based analysis

10.11591/ijict.v14i1.pp11-19
Niaz Ashraf Khan , Md. Ferdous Bin Hafiz , Md. Aktaruzzaman Pramanik
Predictive modelling plays a crucial role in healthcare, particularly in forecasting mortality due to heart failure. This study focuses on enhancing predictive modelling and interpretability in heart failure prediction through advanced boosting algorithms, ensemble methods, and SHapley Additive exPlanations (SHAP) analysis. Leveraging a dataset of patients diagnosed with cardiovascular diseases (CVD), we employed techniques such as synthetic minority over-sampling technique (SMOTE) and bootstrapping to address class imbalance. Our results demonstrated exceptional predictive performance, with the gradient boosting (GBoost) model achieving the highest accuracy of 91.39%. Ensemble techniques further enhanced performance, with the voting classifier (VC), stacking classifier (SC), and Blending achieving accuracies of 91.00%. SHAP analysis uncovered key features such as time, Serum_creatinine, and Ejection_fraction, significantly impacting mortality prediction. These findings highlight the importance of transparent and interpretable machine learning models in healthcare decision-making processes, facilitating informed interventions and personalized treatment strategies for heart failure patients.
Volume: 14
Issue: 1
Page: 11-19
Publish at: 2025-04-01

Enhancing spam detection using Harris Hawks optimization algorithm

10.12928/telkomnika.v23i2.26615
Mosleh; Al-Ahliyya Amman University M. Abualhaj , Sumaya; Al-Ahliyya Amman University Nabil Alkhatib , Ahmad; Al-Ahliyya Amman University Adel Abu-Shareha , Adeeb; Al-Ahliyya Amman University M. Alsaaidah , Mohammed; Universiti Sains Malaysia (USM) Anbar
This paper employs machine learning (ML) algorithms to identify and classify spam emails. The Harris Hawks optimization (HHO) algorithm can detect the crucial features that distinguish spam from ham emails. The HHO algorithm decreased the number of features in the ISCX-URL2016 spam dataset from 72 to 10. Implementing this will enhance the efficiency and cognitive acquisition of the ML algorithms. The decision tree (DT), Naive Bayes (NB), and AdaBoost algorithms are evaluated and contrasted to identify spam emails. The random search algorithm is used to optimize the significant hyperparameters of each algorithm for the specific task of spam identification. All three ML algorithms showed exceptional accuracy in detecting spam emails during the conducted testing. The DT algorithm attained a remarkable accuracy rate of 99.75%. The AdaBoost algorithm ranks second with an incredible accuracy of 99.67%. Finally, the NB algorithm attained an accuracy of 96.30%. The results demonstrate that the HHO algorithm shows promise in recognizing the crucial features of spam emails.
Volume: 23
Issue: 2
Page: 447-454
Publish at: 2025-04-01
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