Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

29,939 Article Results

Simulation of ray behavior in biconvex converging lenses using machine learning algorithms

10.11591/ijeecs.v38.i1.pp357-366
Juan Deyby Carlos-Chullo , Marielena Vilca-Quispe , Whinders Joel Fernandez-Granda , Eveling Castro-Gutierrez
This study used machine learning (ML) algorithms to investigate the simulation of light ray behavior in biconvex converging lenses. While earlier studies have focused on lens image formation and ray tracing, they have not applied reinforcement learning (RL) algorithms like proximal policy optimization (PPO) and soft actor-critic (SAC), to model light refraction through 3D lens models. This study addresses that gap by assessing and contrasting the performance of these two algorithms in an optical simulation context. The findings of this study suggest that the PPO algorithm achieves superior ray convergence, surpassing SAC in terms of stability and accuracy in optical simulation. Consequently, PPO offers a promising avenue for optimizing optical ray simulators. It allows for a representation that closely aligns with the behavior in biconvex converging lenses, which holds significant potential for application in more complex optical scenarios.
Volume: 38
Issue: 1
Page: 357-366
Publish at: 2025-04-01

Enhancing BEMD decomposition using adaptive support size for CSRBF functions

10.11591/ijeecs.v38.i1.pp172-181
Mohammed Arrazaki , Othman El Ouahabi , Mohamed Zohry , Adel Babbah
Despite their widespread development, the Fourier transform and wavelet transform are still unsuitable for analyzing non-stationary and non-linear signals. To address this limitation, bidimensional empirical mode decomposition (BEMD) has emerged as a promising technique. BEMD effectively extracts structures at various scales and frequencies but faces significant computational complexity, primarily during the extremum interpolation phase. To mitigate this, different interpolation functions were presented and suggested, with BEMD using compactly supported radial basis functions (BEMD-CSRBF) showing promising results in reducing computational cost while maintaining decomposition quality. However, the choice of support size for CSRBF functions significantly impacts the quality of BEMD. This article presents an enhancement to the BEMD-CSRBF algorithm by adjusting the CSRBF support size based on the extrema distribution of the image. Our method’s results show a significant improvement in the BEMD-CSRBF algorithm’s quality. Furthermore, when compared to the other two approaches to BEMD, it shows higher accuracy in terms of both intrinsic mode function (IMF) quality and computational efficiency.
Volume: 38
Issue: 1
Page: 172-181
Publish at: 2025-04-01

Automated handwriting analysis and personality attribute discernment using self-attention multi-resolution analysis

10.11591/ijeecs.v38.i1.pp649-656
Yashomati R. Dhumal , Arundhati A. Shinde , Roshnadevi Jaising Sapkal , Satish Bhairannawar
Handwritten document analysis is a method used in academia that examines the patterns and strokes of a person’s handwriting in order to get a deeper understanding of that person’s personality and character. In spite of the fact that there are a number of models and methods that may be used in the investigation of automated graphology, there are a few challenges that need to be solved. Among these challenges is the identification of efficient classification techniques that provide the highest possible degree of accuracy. Within the scope of this study, we propose automated handwriting analysis and personality attribute discernment using self-attention multi-resolution analysis (MRA) where the data is preprocessed using histogram equalization and the spurious line segment section is attached to the genuine line segment portion in order to segment the succeeding line from the authentic picture of the document. A deep dense network is combined with self-attention MRA in order to provide a novel approach to the investigation of authentic handwritten text. Using the most recent and cutting-edge standards that are currently in use, an evaluation is performed to determine whether or not the proposed strategy is feasible. It is observed that the proposed method obtained nearly 98% accuracy with precision of 99%.
Volume: 38
Issue: 1
Page: 649-656
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

Factors influencing the integration of web accessibility in Moroccan public e-services

10.11591/ijict.v14i1.pp77-90
Chadli Fatima Ezzahra , Aniss Moumen , Driss Gretete , Zineb Sabri
Governments worldwide are increasingly digitizing their services to enhance efficiency, transparency, and accessibility for citizens. Morocco has made significant strides in adopting information and communication technology (ICT) and has implemented various initiatives to promote digital transformation across sectors. However, ensuring that digital content and e-services are accessible to everyone, including people with disabilities, is crucial to building an inclusive digital environment. Against this background, this study, based on a qualitative analysis, explores the main factors influencing the integration of web accessibility in the Moroccan public sector from the perspective of web developers and information technology (IT) managers. Through semi-structured interviews and thematic analysis, the findings reveal key barriers such as limited awareness, training deficiencies, and lack of legal framework and available guidelines. Additionally, the study highlights the need for robust managerial backing and greater collaboration with stakeholders, including people with disabilities. By raising awareness and providing actionable insights, this study offers valuable recommendations for policymakers and moves the field forward, providing a foundation for future strategies to enhance web accessibility in the Moroccan public sector.
Volume: 14
Issue: 1
Page: 77-90
Publish at: 2025-04-01

Exploring diverse prediction models in intelligent traffic control

10.11591/ijeecs.v38.i1.pp393-402
Sahira Vilakkumadathil , Velumani Thiyagarajan
Traffic congestion is a major challenge that affects excellence of life for numerous people across world. The fast growth in many vehicles contributes to congestion during peak and non-peak hours. The vehicle traffic resulted in many issues like accidents and inefficiency in traffic flow. Many traffic light control systems operate on fixed time intervals leads to inefficiency. The fixed-time signals cause unnecessary delays on roads with minimum number of quantity vehicles. Intelligent transport systems (ITS) introduce new comprehensive framework that combine the advanced technologies to improve the transportation network efficiency and to optimize the traffic management. The high-traffic routes are forced to wait excessively. Machine learning (ML) methods have designed to examine the traffic control. However, the accurate detection and vehicle tracking are essential one for effective ITS. In order to mention these problems, ML and deep learning (DL) methods are introduced to improve prediction performance.
Volume: 38
Issue: 1
Page: 393-402
Publish at: 2025-04-01

Link adaptation techniques for throughput enhancement in LEO satellites: a survey

10.11591/ijeecs.v38.i1.pp262-271
Habib Idmouida , Khalid Minaoui Minaoui
In addition to the rapid geometric change of low earth orbit (LEO) satellites, the earth-to-space channel suffers from various attenuations that affect the communication link. To overcome this challenge, the link adaptation technique emerges as a key solution to optimize the transmission performance of LEO satellites, especially the data throughput. The existing contributions in the literature remain scattered across the research board, and a comprehensive survey of this research area still lacks at this stage. The present survey examines various link adaptation methods, mainly variable coding and modulation, adaptive coding and modulation, and hybrid methods using artificial intelligence. In addition, this study explains how this technique leverages a set of recommended standards and cost-effective technologies, such as software-defined radio (SDR) and field programmable gate arrays (FPGA), to fine-tune transmission strategies. Lastly, the paper provides a comparative study of the current research on this field and sheds light on future directions, where the need for higher data throughput makes emerging learning-based techniques and new experimental standards a necessity.
Volume: 38
Issue: 1
Page: 262-271
Publish at: 2025-04-01

Recognition of plant leaf diseases based on deep learning and the chemical reaction optimization algorithm

10.11591/ijeecs.v38.i1.pp447-458
Nghien Nguyen Ba , Nhung Nguyen Thi , Dung Vuong Quoc , Cuong Nguyen Cong
Agriculture plays a crucial role in developing countries such as Vietnam, where 70 percent of the population is employed in agriculture, and 57 percent of the social labor force works in the agricultural sector. Therefore, crop productivity directly affects the lives of many people. One of the primary reasons for reduced crop yields is plant leaf diseases caused by bacteria, fungi, and viruses. Hence, there is a need for a method to help farmers identify leaf diseases early to take appropriate action to protect crops and shift to smart agricultural production. This paper proposes lightweight deep learning (DL) models combined with a support vector machine (SVM), with hyperparameters fine-tuned by chemical reaction optimization (CRO), for detecting plant leaf diseases. The main advantage of the method is the simplicity of the architecture and optimization of the DL model’s hyperparameters, making it easily deployable on low hardware devices. To test the performance of the proposed method, experiments are performed on the PlantVillage dataset using Python. The superiority of the proposed method over the well-known visual geometry group-16 (VGG-16) and MobileNetV2 models is demonstrated by a 10% increase in accuracy prediction and a decrease of 5% and 66% in training time, respectively.
Volume: 38
Issue: 1
Page: 447-458
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

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

Boosting industrial internet of things intrusion detection: leveraging machine learning and feature selection techniques

10.11591/ijai.v14.i2.pp1232-1241
Lahcen Idouglid , Said Tkatek , Khalid Elfayq
The rapid integration of industrial internet of things (IIoT) technologies into Industry 4.0 has revolutionized industrial efficiency and automation, but it has also exposed critical vulnerabilities to cyber threats. This paper delves into a comprehensive evaluation of machine learning (ML) classifiers for detecting anomalies in IIoT environments. By strategically applying feature selection techniques, we demonstrate significant enhancements in both the accuracy and efficiency of these classifiers. Our findings reveal that feature selection not only boosts detection rates but also minimizes computational demands, making it a cornerstone for developing resilient intrusion detection systems (IDS) tailored for Industry 4.0. The insights garnered from this study pave the way for deploying more robust security frameworks, safeguarding the integrity and reliability of IIoT infrastructures in modern industrial settings.
Volume: 14
Issue: 2
Page: 1232-1241
Publish at: 2025-04-01

Multimodal perception for enhancing human computer interaction through real-world affect recognition

10.11591/ijeecs.v38.i1.pp428-438
Karishma Raut , Sujata Kulkarni , Ashwini Sawant
Human-Computer Interaction can benefit from real-world affect recognition in applications like healthcare and assistive robotics. Human express emotions through various modalities, with audio-visual being the most significant. Using a unimodal approach, such as only speech or visual, is challenging in natural, dynamic environments. The proposed methodology integrated a pretrained model with a convolution neural network (CNN) to provide a robust initialization point and address the limited availability of facial expression data. The multimodal framework enhances discriminative power by combining visual scores with speech. This work addresses the challenges at each stage of the real-world affect recognition framework, including data preprocessing, feature extraction, feature fusion, and final classification. A 1D-CNN is employed for training on spectral and prosodic audio features, while deep visual features are processed using a 2D-CNN. The proposed system's performance was evaluated on the extended Cohn-Kanade (CK+), acted-facial-expressions in-the-wild (AFEW), and real-world-affective-face-database (RAF) datasets, which are commonly used in face recognition research. Experimental results indicate that 2% to 5% of visual data from natural settings were undetected, and the inclusion of the audio modality improved performance by providing relevant and supplementary information.
Volume: 38
Issue: 1
Page: 428-438
Publish at: 2025-04-01

Implementation of a prototype to prevent childhood accidents in dangerous domestic environments using ESP 32 Wi-Fi module

10.11591/ijeecs.v38.i1.pp88-98
Jenner Lavalle-Sandoval , Paul Córdova-Cardenas , Sheyla Rivera-Quispe , Laberiano Andrade-Arenas
Robotics has significantly advanced human evolution by optimizing tasks in fields such as medicine, engineering, and mechanics, enhancing daily life through various robotic prototypes. These innovations help prevent accidents and injuries, whether at home or in hazardous environments. For instance, sensors can detect gas leaks, fires, and other potential disasters. This research aims to design a prototype adaptable to any home environment that poses risks to infants, such as kitchens, bathrooms, or stairs. The proposed prototype incorporates gas, motion, and sound sensors connected to a Wi-Fi ESP 32 module, which alerts parents to any potential danger to their children. The research is developed in six phases: component selection, circuit simulation, prototype design, three-dimensional (3D) printing, code programming, and final testing. The results demonstrate a positive impact, improving the control and care of infants by alerting parents to hazards such as gas leaks, crying, or movement in risky areas. The conclusion confirms the effectiveness of the prototype in providing timely alerts to safeguard infants in potentially dangerous situations.
Volume: 38
Issue: 1
Page: 88-98
Publish at: 2025-04-01

Sentiment analysis based on Indonesian language lexicon and IndoBERT on user reviews PLN mobile application

10.11591/ijeecs.v38.i1.pp677-688
Yessy Asri , Dwina Kuswardani , Widya Nita Suliyanti , Yosef Owen Manullang , Atikah Rifdah Ansyari
PLN mobile application as an integrated platform for self-service among mobile consumers, facilitating easier access to various services, including receiving information such as public complaints. The application can be downloaded through the Google Play Store and App Store, and users can express their opinions through reviews and ratings. In this era of advanced technology, aspects such as reviews, ratings, and evaluations have important value for business practitioners. However, there are often inconsistencies between ratings and reviews that do not fully represent the quality of the application. In response, a study was conducted to analyze the sentiment of user reviews from January to June 2022, by collecting 1,000 review samples from the Google Play Store. The data was collected using web scraping techniques and then processed into a dataset through text pre-processing methods. Sentiments were analyzed using an automatic labeling method in Indonesian based on a lexicon known as INSET (Indonesia sentiment), which resulted in 482 positive reviews, 144 negative reviews, and 374 neutral reviews. The next step is classification using Indonesian bidirectional encoder representations from transformers (IndoBERT). In this process, the data was divided into testing, training, and validation sets with a ratio of 80:10:10. The analysis managed to achieve an impressive accuracy rate of 81%.
Volume: 38
Issue: 1
Page: 677-688
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
1 2 3
...
1995 1996
Show 221 of 1996

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration