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31,456 Article Results

Integration of message queue and drop policy in spray and hop distance protocol for DTNs in smart city scenario

10.11591/ijeecs.v37.i2.pp839-848
Agussalim Agussalim , Masato Tsuru , I Gede Susrama Mas Diyasa , Basuki Rahmat
Implementing delay tolerant networks (DTNs) in smart cities for developing countries is promising. DTNs offer a low-cost network communication solution without expensive infrastructure, such as 3G/4G or LPWA networks provided by commercial operators. This paper considers sensor data collection in the Surabaya smart city scenario, and the use of DTNs is examined instead of the expensive infrastructure. However, a customized and optimized protocol is required since no existing DTN routing protocol perfectly matches the scenario. This paper proposes integrating a hop count-based message queue (HCMQ) and a message time-to-live (TTL)-based drop policy (MTDP) into the spray and hop distance protocol (SNHD), an enhanced version of the spray and wait (SNW) protocol. The Surabaya smart city scenario was simulated on the one simulator with a wide range of message generation rates at each sensor node. The proposed integration significantly improves the total size of delivered messages, especially when the message generation rate is high, i.e., in congested situations, compared to other routing protocols in this scenario. It also exhibits an average latency lower than other routing protocols. Overall, this integration enhances the DTNs protocol’s performance in a low-cost alternative data collection in the Surabaya smart city scenario.
Volume: 37
Issue: 2
Page: 839-848
Publish at: 2025-02-01

A novel model to detect and categorize objects from images by using a hybrid machine learning model

10.11591/ijai.v14.i1.pp667-679
Nilambar Sethi , Vetukuri Venkata Siva Rama Raju , Venkata Srinivas Lokavarapu , Ravi Babu Devareddi , Shiva Shankar Reddy , Silpa Nrusimhadri
As humans, we can easily recognize and distinguish different features of objects in images due to our brain’s ability to unconsciously learn from a set of images. The objectives of this effort are to develop a model that is capable of identifying and categorizing objects that are present within images. We imported the dataset from Keras and loaded it using data loaders to achieve this. We then utilized various deep learning algorithms, such as visual geometry group (VGG)-16 and a simple net-random forest hybrid model, to classify the objects. After classification, the accuracy obtained by VGG16 and the hybrid model was 84.7% and 89.6%, respectively. Therefore, the proposed model successfully detects objects in images using a simple net as a feature extractor and a random forest for object classification, achieving better accuracy than VGG16.
Volume: 14
Issue: 1
Page: 667-679
Publish at: 2025-02-01

Novel method for multi-user collaborative spectral decision in decentralized cognitive radio networks

10.11591/ijeecs.v37.i2.pp888-902
Cesar Hernández , Diego Giral , Camila Salgado
Cognitive radio networks positively impact the performance of wireless communications and have proven to be an excellent alternative for efficient and effective use of the radio spectrum. However, few proposals collaboratively work on decision-making in decentralized cognitive radio networks. The present work refers to a novel method and device that reduces the rate of channel changes during secondary user communications in decentralized cognitive radio networks through a collaborative spectral decision between several secondary users while allowing multiple secondary users access to the network. This proposal consists of a multi-user unit that regulates the access of multiple secondary users (SUs) to the spectrum, a priority unit that guarantees timely access to the SUs according to their level of importance, and a prediction unit that forecasts the arrival time of the primary user (PU). This multichannel unit regulates the assignment of multiple spectral opportunities to the SU according to the type of application it is using and a unit of deep learning that determines which spectral opportunity(s) are most suitable for each SU and spectral allocation. The results obtained allow us to satisfactorily validate the proposal developed and corroborate the importance of collaborative work in decision-making to select spectral opportunities.
Volume: 37
Issue: 2
Page: 888-902
Publish at: 2025-02-01

High-gain circularly polarized metasurface antenna for NR257 band millimeter-wave 5G communication

10.11591/ijeecs.v37.i2.pp867-877
Praveen Haandi Lakshman , Yerriswamy Thatti , Puneeth Kumar Tharehalli Rajanna , Shambulinga Mudukavvanavar
A metasurface inspired circularly polarized (CP) compact patch antenna with high gain for fifth-generation communication systems is designed and implemented in this article. The proposed structure features a corner truncated patch antenna and a metasurface of 3×3 double sided identical circular metallic patches. The attribute that puts this design distinct is that it minimizes the impact of scattering and edge diffraction at millimeter wave frequencies. The metasurface above a patch with an air gap is designed using the similar substrate material with the same thickness, resulting in a simplified antenna design with high gain and low cost. The antenna’s overall dimension is , with a peak gain of 11.5 dBic and a 3-dB axial ratio bandwidth of 28.45 - 28.88 GHz. The simulated and experimental results show that the metasurface-inspired antenna has better impedance matching and radiation efficiency between 28.23 - 30.01 GHz. Additionally, the experimental results of the proposed antenna exhibits stable right-hand circular polarization in the desired frequency range and a flat gain response with a little variation. The proposed antenna design could be well suited for millimeter-wave communication systems, in scenarios requiring robust long-range performance and high data throughput.
Volume: 37
Issue: 2
Page: 867-877
Publish at: 2025-02-01

Evaluating low-cost internet of things and artificial intelligence in agriculture

10.11591/ijeecs.v37.i2.pp968-975
Kamal Elhattab , Said Elatar
This article investigates the transformative impact of low-cost internet of things (IoT) solutions on the agricultural sector, with a particular emphasis on integrating artificial intelligence (AI) and machine learning (ML) technologies. The study aims to illustrate how affordable IoT technologies, when combined with advanced AI and ML capabilities, can serve as a significant asset for small and medium-sized farms. It addresses the economic and technical barriers these farms face in adopting such technologies, including high initial costs and the complexity of implementation. By conducting a comprehensive evaluation of existing IoT hardware and software, the research identifies and highlights innovative, cost-effective solutions that have the potential to drive significant advancements in agricultural practices. The findings underscore how these integrated technologies can enhance operational efficiency, increase productivity, and support sustainable agricultural development. Additionally, the paper explores the potential challenges and limitations of adopting these technologies, offering insights into how they can be mitigated. Overall, the study demonstrates that the convergence of low-cost IoT with AI and ML presents a valuable opportunity for modernizing agriculture and improving farm management.
Volume: 37
Issue: 2
Page: 968-975
Publish at: 2025-02-01

EDK-LEACH: improving LEACH protocol-based machine learning in wireless sensor networks

10.11591/ijeecs.v37.i2.pp1251-1261
Taous Lechani , Samia Ourari , Fayçal Rahmoune , Said Amari , Hayet Termeche
In wireless sensor networks (WSNs), many sensor devices are spread throughout the environment with the goal of collecting data and sending them to a base station (BS) for further studies. The issue of their limited battery power has aroused the interest of researchers, and several protocols were developed to optimize energy use and thus increase the network’s lifetime. The present research enhances the well-known low-energy adaptive clustering hierarchy (LEACH) protocol with a new artificial intelligence (AI) protocol named energy distance K-means LEACH (EDK-LEACH). For this purpose, an innovative clustering strategy built on the machine learning K-means algorithm is used in WSNs to improve the cluster formation process and maximise network stability. By implementing an objective function that considers each node’s residual energy and distance from the cluster centre when selecting the cluster head (CH) of each cluster, EDK-LEACH also eliminates the inherent randomness in LEACH during the CH election process. The proposed protocol has the advantage of ensuring better CH distribution throughout the network surface with a balanced load across all network nodes. In comparison with the known LEACH, the simulation results demonstrate the efficiency of our approach: the lifetime of the network is extended and the energy consumption is reduced.
Volume: 37
Issue: 2
Page: 1251-1261
Publish at: 2025-02-01

Designing fuzzy membership functions using genetic algorithm with a new encoding method

10.11591/ijeecs.v37.i2.pp781-788
Ali Hamed , Slimane Hireche , Abdelkader Bekri , Ahmed Cheriet
This article presents a new method for designing fuzzy membership functions using the genetic algorithm (GA) without the use of constraints. Conventional approaches to designing these functions often involve manual tuning or optimization techniques with limitations. However, this article introduces a constraint-free approach, as the GA requires all constraints to be met for a chromosome; if even one condition is not satisfied, the chromosome is discarded, regardless of its ideal values for other variables. Consequently, a high number of constraints, especially in the studied case, increases the likelihood of chromosome rejection, leading to a time-consuming design process and suboptimal results.
Volume: 37
Issue: 2
Page: 781-788
Publish at: 2025-02-01

Convolutional neural network modelling for autistic individualized education chatbot

10.11591/ijai.v14.i1.pp109-118
Raseeda Hamzah , Nursuriati Jamil , Nor Diana Ahmad , Syed Mohd Zahid Syed Zainal Ariffin
The traditional education system for autistic kids needs integration with computer technology that embraces artificial intelligence to help school instructors and management. An application that enables the teacher to retrieve information from a trusted source is essential since the information is only sometimes available on time. Thus, developing a chatbot application that utilizes natural language processing can enhance the management of autistic schools and will help individualized education for autistic students. This research uses a deep learning model that utilizes a convolutional neural network to develop a chatbot as a teaching assist tool for teachers. The results show that the chatbot has achieved ˜0.03% loss when trained with different epoch numbers. In terms of usability, the chatbot achieves mean system usability scores of 80.48 ± 13.03. This may open opportunities for more effective individualized education for students with special needs and increase the potential to improve inclusive education for disabled students. It is useful to include future actions that enable the simplification of the use of this chatbot tool in a wide range of contexts. To close the education gap for children with disabilities, chatbots could help people with communication disabilities and could also significantly enhance the rate of communication.
Volume: 14
Issue: 1
Page: 109-118
Publish at: 2025-02-01

Design of starting a three phase induction motor using direct on-line, variable frequency drive, soft starting, and auto transformer methods

10.11591/ijeecs.v37.i2.pp700-714
Yulianta Siregar , Yosephine Rotua Oktaviana Siahaan , Nur Nabila Binti Mohamed , Dedet Candra Riawan , Muldi Yuhendri
The problem with 3-phase induction motors is that when starting the motor, the motor starting current can reach five to seven times the nominal current. This research compares slip, starting current, bus voltage, acceleration torque, motor torque, energy savings, and kVAR from the direct on-line (DOL), variable frequency drive (VFD), soft starting, and autotransformer starting methods in the electrical transient analyzer program (ETAP) software. This research result shows that the fastest VFD slip reaches a steady state, namely at 11+ seconds. The lowest starting/starting current is owned by the VFD method, namely <20% full load amps (FLA) in the first 2 seconds. The lowest decrease in bus voltage at steady state was experienced by the VFD method, namely 0.8152%. The quickest acceleration torque reaches a steady state in the VFD method, namely in 11+ seconds. The soft starting method owns the lowest starting torque, namely 20.75%. The soft starting method has the largest energy savings, namely 148.02 kW. Of the several variables observed, the best starting method is the VFD method.
Volume: 37
Issue: 2
Page: 700-714
Publish at: 2025-02-01

Imitation of the human upper limb by convolutional neural networks

10.11591/ijai.v14.i1.pp193-203
Paula Useche Murillo , Robinson Jimenez Moreno , Javier Eduardo Martinez Baquero
The paper outlines the development of an algorithm focused on imitating movements of a human arm and replicating strokes generated by the user's hand within a working environment. The algorithm was crafted to discern the position of either the user's left or right arm, tracking each section (fingers, wrist, elbow, and shoulder) through a detection and tracking system. These movements are then replicated onto a virtual arm, simulating the actions of a cutting tool, generating strokes as it moves. Convolutional neural networks (CNNs) were employed to detect and classify each arm section, while geometric analysis determined the rotation angles of each joint, facilitating the virtual robot's motion. The stroke replication program achieved an 84.2% accuracy in stroke execution, gauged by the closure of the polygon, distance between initial and final drawing points, and generated noise, which was under 10%, with a 99% probability of drawing a closed polygon. A Fast region-based convolutional neural network (Fast R-CNN) network detected each arm section with 60.2% accuracy, producing detection boxes with precision ranging from 17% to 59%. Any recognition shortcomings were addressed through mathematical estimation of missing points and noise filters, resulting in a 90.4% imitation rate of human upper limb movement.
Volume: 14
Issue: 1
Page: 193-203
Publish at: 2025-02-01

Evaluation of ChatGPT research in STEAM education

10.11591/ijere.v14i1.30423
Binar Kurnia Prahani , Khoirun Nisa’ , Suliyanah Suliyanah , Utama Alan Deta
Research focus of reviews trends and research on implementing ChatGPT in science, technology, engineering, arts, and math (STEAM) learning. It emphasizes the importance of deep learning and 21st-century skills in education, highlighting the limitations of ChatGPT in accuracy and credibility. The authors analyzed 204 STEAM education documents, revealing that 65% focused on technology education and less than 3% on art and mathematics education. The articles written in technology scope are the most widely circulated. The most productive region is the United States, which has three productive authors. The most productive authors are Ray (India) and Wang (Macao), who have the highest h-index. The United States and United Kingdom are the most productive affiliations. Many types of research on ChatGPT in STEAM education include a survey with several participants of different education levels. Social science is the most popular subject area. The Journal Nature is the primary source for this research. Several research highlighted artificial intelligence, ChatGPT, and human keywords. This study highlights the potential of ChatGPT in STEAM, suggesting further research on student behavior, learning designs, and credibility concerns. It suggests collaboration with Google Scholar or Web of Science data for in-depth analysis.
Volume: 14
Issue: 1
Page: 598-611
Publish at: 2025-02-01

Systematic review for attack tactics, privacy, and safety models in big data systems

10.11591/ijeecs.v37.i2.pp1234-1250
Majdoubi Chaymae , Gahi Youssef , El Mendili Saida
This systematic review explores cyberattack tactics, privacy concerns, and safety measures within big data systems, focusing on the critical challenges of maintaining data security in today's complex digital environments. The review begins by categorizing various cyberattacks, laying the groundwork for understanding the threats to big data. It identifies key vulnerabilities that compromise privacy and safety, and examines the ethical implications of these issues. The role of artificial intelligence in enhancing security defenses is highlighted as a crucial aspect of mitigating these threats. Additionally, a comparative assessment of regulatory frameworks such as GDPR, NIST, and ISO 27001 is provided, emphasizing the importance of legal and compliance considerations in data protection. The review concludes by proposing a structured approach to cyberattack detection and processing, advocating for strategies that address both technical vulnerabilities and regulatory requirements, followed by a critical discussion on the usability of previous methods for mobile security, highlighting adaptability and performance, discussing explainability and Gen AI adoption. This work offers valuable insights for researchers, practitioners, and policymakers, contributing to the ongoing discourse on cybersecurity in the big data era.
Volume: 37
Issue: 2
Page: 1234-1250
Publish at: 2025-02-01

A novel CNN-ANN fusion approach for improved facial emotion detection

10.11591/ijeecs.v37.i2.pp959-967
Viraj Sawant , Husna Shaikh , Bhakti Palkar , Sanam Kazi , Wasim Jasani , Lamiya Rampurawala , Mohammed Naser Shaikh
In recent years, the field of emotion recognition has witnessed an increased interest due to the rise of deep learning techniques. However, one of the persistent difficulties in this domain, which we have attempted to address, is the variability in image sizes utilized. In this study, we have reviewed the work by different researchers and summarized their key findings. In our research, we introduce a novel technique that integrates the strengths of 1D convolutional neural networks (CNNs) and artificial neural networks (ANNs) through a late fusion model, leveraging CNNs' shared weights and automatic feature learning for spatial and temporal data, alongside ANN's comprehensive feature consideration. Our research findings highlight the effectiveness of this approach, which achieves a remarkable accuracy of 92.42%, along with other evaluation metrics demonstrating notable results. Furthermore, we conduct a comprehensive analysis of the proposed method, comparing it with advanced methods in the field of facial emotion recognition. Through this comparative analysis, we demonstrate the superiority of our proposed approach, addressing challenges that have not yet been addressed till date, thus leading to progress in this field.
Volume: 37
Issue: 2
Page: 959-967
Publish at: 2025-02-01

Collaborative praxis learning to empower rural area: an action research

10.11591/ijere.v14i1.27716
Abdul Aziz , Ahmad Barizi , Syuhadak Syuhadak , Kususanto Ditto Prihadi , Hamim Hamim , Eliya Nur khayati , Syaiful Mustofa
The emergence of collaborative learning praxis among adult learners has gained prominence, fostering lifelong practical learning. This approach empowers adults to define their learning goals based on interests and needs, facilitating contextual information sharing, knowledge acquisition, and skill enhancement. Such an approach promotes innovation and creativity, strengthening market competitiveness. This study focuses on collaborative learning’s impact in the village of Sukodono, East Java, Indonesia, post-COVID-19. Collaborative learning facilitated skill development, innovation, and market influence. Action research was employed to restore economic well-being. Themes of healthy environment preservation, community self-help, and market power were explored. The villagers’ adoption of biogas technology, digital platforms for product promotion, and focus on high-demand products showcased the efficacy of collaborative learning. This praxis enhances economic conditions and knowledge acquisition, enabling sustainable community development. Further quantitative studies are recommended to assess long-term impacts.
Volume: 14
Issue: 1
Page: 749-757
Publish at: 2025-02-01

Privacy preserving ZK-STARK based blockchain for agriculture food supply chain

10.11591/ijeecs.v37.i2.pp1102-1111
Madhuri Sadashiv Arade , Nitin N. Pise
The blockchain-based applications such as food supply chain (FSC), healthcare are becoming increasingly popular due to their decentralized nature, distributed structure, and the ability to track products. Still, there are concerns regarding the privacy of transacted data, including personal identities, as all transactions are recorded on a ledger accessible to participating nodes. Existing technologies of privacy preservation are vulnerable to quantum attacks, which will pose a significant threat to blockchain applications in the future. To address this, a proposed model uses modified zero-knowledge scalable transparent argument of knowledge (ZK-STARK) in the blockchain FSC by utilizing three different polynomial interpolation methods. Performance measurements indicate that the fast fourier transform (FFT) outperforms the others. Unlike ZK-SNARK, ZK-STARK does not require a trusted setup, makes scalable and transparent. By defending against quantum attacks, this model enhances the security of the blockchain system. The blockchain-based FSC is implemented using hyperledger composer, with all entities completing transactions privately through ZK-STARK and smart contracts. But, ZK-STARK may add performance overhead into the blockchain FSC. Future work will aim to reduce the performance overhead of ZK-STARK, decide which operations should be off-chain or on-chain, and compare the performance of this new model to the existing system.
Volume: 37
Issue: 2
Page: 1102-1111
Publish at: 2025-02-01
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