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31,456 Article Results

An innovative image encryption scheme integrating chaotic maps, DNA encoding and cellular automata

10.11591/ijeecs.v39.i1.pp710-719
Gaverchand Kukaram , Venkatesan Ramasamy , Yasmin Abdul
In the current digital era, securing image transmission is crucial to ensure data integrity, prevent tampering, and preserve confidentiality as images traverse unsecured channels. This paper presents an innovative encryption scheme that synergistically combines a two-dimensional (2-D) logistic map, deoxyribonucleic acid (DNA) encoding, and 1-D cellular automata (CA) rules to significantly bolster encryption robustness. The proposed model initiates with the generation of a key image via the 2-D logistic map, yielding intricate chaotic sequences that fortify the encryption mechanism. DNA cryptography is employed to amplify randomness through diffusion properties, providing robust defense against various cryptographic attacks. The integration of 1-D CA rules further intensifies encryption complexity by iteratively processing DNA-encoded sequences. Experimental results substantiate that the proposed encryption scheme demonstrates exceptional endurance against a vast spectrum of attacks, affirming its superior security.
Volume: 39
Issue: 1
Page: 710-719
Publish at: 2025-07-01

Evolution of the optical add/drop multiplexer in dense wavelength division multiplexing optical networks

10.11591/ijeecs.v39.i1.pp247-257
Mnotho P. Mkhwanazi , Khumbulani Mpofu , Vusumuzi Malele
Mobile network operators are facing ever-increasing traffic demands because of the numerous data-hungry applications used by subscribers nowadays. As a result, technologies that support high bandwidth and network availability have become essential. One such technology is dense wavelength division multiplexing (DWDM). This study investigated the evolution of an optical add/drop multiplexer (OADM), which is one of the key components of DWDM technology. The goal of this research was to investigate how the evolution of an OADM has contributed to network survivability and bandwidth enhancement in DWDM optical networks. A thorough search of the literature on an OADM was undertaken using data sources like Google Scholar, Elsevier, ResearchGate, ScienceDirect, Springer, and DWDM vendor manuals. The study found that in order to address present and future DWDM optical network demands, a reconfigurable optical add/drop multiplexer (ROADM) deployed over flexgrid spectrum is essential. The most advanced iteration of a ROADM supports colorless, directionless, contentionless, and flex-grid functionalities, resulting in the most robust, flexible, and future-proof DWDM optical network. The study further found that flex-grid technology supports uplinks with high line rates and has superior spectral efficiency.
Volume: 39
Issue: 1
Page: 247-257
Publish at: 2025-07-01

Boosting real-time vehicle detection in urban traffic using a novel multi-augmentation

10.11591/ijeecs.v39.i1.pp656-668
Imam Ahmad Ashari , Wahyul Amien Syafei , Adi Wibowo
Real-time vehicle object detection in urban traffic is crucial for modern traffic management systems. This study focuses on improving the accuracy of vehicle identification and classification in heavy traffic during peak hours, with particular emphasis on challenges such as small object sizes and interference from light reflections. The use of multi-label images enables the simultaneous detection of various vehicle types within a single frame, providing more detailed information about traffic conditions. You only look once (YOLO) was chosen for its capability to perform real-time object detection with high accuracy. Multi-augmentation techniques were applied to enrich the training data, making the model more robust to varying lighting conditions, viewpoints, object occlusions, and issues related to small objects. YOLOv8n and YOLOv9t were selected for their speed and efficiency. Models without augmentation, 10 single-augmentation techniques, and 5 multi-augmentation techniques were tested. The results show that YOLOv8n with multiaugmentation (scaling, zoom in, brightness adjustment, color jitter, and noise injection) achieved the highest mAP50-95 score of 0.536, surpassing YOLOv8n with single-augmentation Blur, which had an mAP50-95 of 0.465, as well as YOLOv8n without augmentation, which scored 0.390. Multiaugmentation proved to significantly enhance YOLO’s performance.
Volume: 39
Issue: 1
Page: 656-668
Publish at: 2025-07-01

Smart enterprise architecture framework for developing patent office

10.11591/ijeecs.v39.i1.pp681-690
Yoga Prihastomo , Harjanto Prabowo , Agung Trisetyarso , Haryono Soeparno
Technology and communication’s impact on daily life makes innovation vital for economic growth, highlighting prizing intellectual property (IP) asset protection and management. Patent office, pivotal custodians of legal frameworks and repositories of IP assets, grapples with significant challenges, and backlogs stemming from escalating patent applications and outdated processes. Patent office encounters the challenge of balancing innovation and IP protection because of the convergence of rapid advancements in technologies, for instance, AI, and blockchain. This research employs a design science research methodology to generate a tailored framework addressing these multifaceted challenges. The proposed smart enterprise architecture (SEA) framework offers a strategic, multidimensional approach to modernizing the patent office. It integrates principles from enterprise architecture, information systems management, and IP law, emphasizing efficiency, scalability, and security. The framework leverages the quadruple helix model, fostering collaboration between government, industry, academia, and civil society to enhance stakeholder engagement and innovation ecosystems. Optimizing patent office functions and adapting to IP management’s evolution, the SEA framework integrates technology and organizational goals for a comprehensive approach.
Volume: 39
Issue: 1
Page: 681-690
Publish at: 2025-07-01

Unraveling the relationships among essential oil compounds in Aquilaria species using GC-MS and GC-FID techniques

10.11591/ijeecs.v39.i1.pp167-177
Nur Athirah Syafiqah Noramli , Noor Aida Syakira Ahmad Sabri , Muhammad Ikhsan Roslan , Nurlaila Ismail , Zakiah Mohd Yusoff , Mohd Nasir Taib
Agarwood, a prized non-timber resource from the Aquilaria genus, is highly valued for its aromatic and medicinal properties, playing a significant role in the healthcare, fragrance, and pharmaceutical industries. This research analyzes essential oils from four Aquilaria species-A. beccariana, A. malaccensis, A. crassna, and A. subintegra-using gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detection (GC-FID). The primary objective is to optimize classification efficiency by reducing computational time and reducing multicollinearity through feature selection. Pearson correlation analysis revealed strong relationships among six chemical compounds-β-selinene (A), dihydro-β-agarofuran (B), δguaiene (C), 10-epi-γ-eudesmol (D), γ-eudesmol (E), and pentadecanoic acid (F). Through feature selection, the three most significant compoundsdihydro-β-agarofuran (B), γ-eudesmol (D), and 10-epi-γ-eudesmol (E)-were identified, achieving a remarkable 90.02% reduction in computational time (from 0.0403 to 0.0040 seconds). These findings highlight the effectiveness of structured feature selection in refining essential oil profiling and enhancing species classification accuracy. Future research directions include exploring machine learning-based feature selection techniques to further streamline feature reduction processes and expand the scope of essential oil authentication. This study contributes to advancing the scientific understanding and practical utilization of agarwood essential oils, paving the way for more efficient and reliable analytical frameworks.
Volume: 39
Issue: 1
Page: 167-177
Publish at: 2025-07-01

Real-time driver drowsiness detection based on integrative approach of deep learning and machine learning model

10.11591/ijeecs.v39.i1.pp592-602
Gowrishankar Shiva Shankara Chari , Jyothi Arcot Prashant
Driver drowsiness is a major factor that contributing to road accidents. Several researches are ongoing to detect driver drowsiness, but they suffer from the complexity and cost of the models. This paper introduces a hybrid artificial intelligence (AI)-driven framework integrating deep learning (DL) and machine learning (ML) models for real-time drowsiness detection. The system utilizes a robust DL model to classify driver states based on facial images and support vector machine (SVM) model is trained to develop a cost-efficient yet robust facial landmark detector to extract key features such as eye aspect ratio (EAR) and mouth aspect ratio (MAR). We also introduce a multi-stage decision fusion mechanism that combines convolutional neural network (CNN) probability scores with EAR/MAR thresholds to enhance detection reliability and reduce false positives. Experimental results demonstrate that the proposed model achieves 98% accuracy and F1-score, significantly outperforming traditional DL approaches. Additionally, the SVM-based landmark predictor shows improved efficiency with lower mean squared error (MSE) without having higher computational requirements.
Volume: 39
Issue: 1
Page: 592-602
Publish at: 2025-07-01

Design and development of an automated spirulina (Arthrospira platensis) algae cultivator

10.11591/ijeecs.v39.i1.pp139-147
Miguel Q. Mariñas II , Mark Joseph B. Enojas , Daryll C. Balolong , Charissa Zandra B. Correa , Lemmuel Keith C. Roldan , Mark Lester Teves , Christian Mari Dela Cruz
The cultivation of algae has gotten more attention from algae enthusiasts who have seen the benefits of algae in many uses. To maximize productivity, the parameters for growth of this algae must be controlled, such as pH, turbidity, light intensity, and the mixture solution for optimal growth. In this paper, an automated spirulina algae cultivator is designed and developed in a small-scale pond to replace the existing manual process. The system developed is composed of compact and modular cultivation unit, ph sensor, water level sensor, turbidity sensor, light intensity sensor, and motor actuators for mixing solutions. Each parameter was controlled individually in an on-off control system. A simple nutrient addition program (SNAP) solution is also used for better growth productivity by maximizing its nutrient contents. The pH is maintained at 9 to 12 for a healthy biomass output. Daily weight measurement was conducted using an analytical balance to monitor its growth. Using the developed prototype recorded a 33% higher rate of productivity over the manual process. This setup can potentially be used as a model for mass production of spirulina algae.
Volume: 39
Issue: 1
Page: 139-147
Publish at: 2025-07-01

Web GIS-based postcode alternative system for resolving “last mile” problem in Jordan’s home delivery

10.11591/ijeecs.v39.i1.pp531-544
Firas Omar , Ahmad Nabot , Bilal Sowan
As more and more people shop online, the postal code system must be more dependable. Due to the absence of a comprehensive postcode system, online purchases and shipping in the developing country of Jordan are complicated. This research paper proposes an alternative delivery system for delivering online purchases to customers without postal codes. Smartphone and computer-based geographic information system (GIS) applications evaluated in Jordan. The scientists found that the users were eager to adopt the system based on its ease of use and adoption rate. A questionnaire survey was distributed to 167 retail stores, delivery logistics employees, university students, and academics. The data collected were then analyzed using SPSS techniques such as POST HOC and ANOVA. To find a home delivery solution, we tested the suggested system app on both desktop and Smartphone platforms. The findings show that it is easier to locate a residential neighborhood. Customer trust and satisfaction with online purchases should increase due to the additional benefits of the system installation. Improve the effectiveness of home delivery services in Jordan with the use of artificial intelligence (AI). Both customers and stores prefer this system for online shopping rather than using postcodes. According to these data, experts can enhance their items by implementing digital sales strategies.
Volume: 39
Issue: 1
Page: 531-544
Publish at: 2025-07-01

An improved efficientnet-B5 for cucurbit leaf identification

10.11591/ijeecs.v39.i1.pp336-344
Quang Hung Ha , Trong-Minh Hoang , Minh Trien Pham
Plant diseases significantly impact the quality and productivity of crops, leading to substantial economic losses. This paper introduces two enhanced EfficientNet-B5 architectures, EfficientNetB5-sigca and EfficientNetB5- sigbi, specifically designed to detect and classify diseases in cucurbit leaves. We employ EfficientNet-B5 for feature extraction, using a 456×456×3 input and omitting the top layer to generate feature maps with Swish activation. A global average pooling 2D layer replaces the conventional fully connected layer, producing a flattened vector. This is followed by a dense layer with four output units, L2 regularization, and sigmoid activation, using either categorical or binary cross-entropy as the loss function. We also developed a novel image dataset targeting cucumber and cantaloupe leaves, including 11,425 augmented images categorized into four disease classes: anthracnose, powdery mildew, downy mildew, and fresh leaf. Our experiments dataset demonstrates that the EfficientNetB5-sigbi achieves an accuracy of 97.07%, marking a significant improvement in classifying similar diseases in cucurbit leaves.
Volume: 39
Issue: 1
Page: 336-344
Publish at: 2025-07-01

A sentiment analysis on skewed product reviews: Ben & Jerry's ice cream

10.11591/ijeecs.v39.i1.pp364-373
Nabilla Nurulita Dewi , Sekar Gesti Amalia Utami , Shalsabila Aura Adiar , Hasan Dwi Cahyono
Sentiment analysis of product reviews offers valuable insights into consumer perspectives, which can inform product development and marketing strategies. Given the growing importance of user-generated content like product reviews, this study explored sentiment classification in online reviews of Ben & Jerry's ice cream. We designed and evaluated three machine learning algorithms for sentiment classification: Naïve Bayes (NB), logistic regression (LR), and support vector machine (SVM). The dataset exhibited a significant class imbalance, with substantially more positive than negative reviews. We employed two oversampling techniques: the synthetic minority oversampling technique (SMOTE) and the adaptive synthetic sampling approach (ADASYN). With the original skewed data, NB, LR, and SVM achieved accuracies of 91.90%, 93.77%, and 95.09%, respectively. While SMOTE did not improve performance in some scenarios, ADASYN yielded positive results and generally enhanced model reliability across all algorithms. Post-balancing with ADASYN, the sentiment distribution became less skewed, and accuracies shifted to 92.04% for NB, 94.96% for LR, and 95.23% for SVM. The combination of SVM and ADASYN demonstrated promising results, suggesting this approach may offer robust and efficient performance for binary sentiment classification, especially with imbalanced datasets.
Volume: 39
Issue: 1
Page: 364-373
Publish at: 2025-07-01

Robust k-NN approach for classifying Aquilaria oil species by compounds

10.11591/ijeecs.v39.i1.pp178-189
Noor Aida Syakira Ahmad Sabri , Nur Athirah Syafiqah Noramli , Nik Fasha Edora Nik Kamaruzaman , Nurlaila Ismail , Zakiah Mohd Yusoff , Ali Abd Almisreb , Saiful Nizam Tajuddin , Mohd Nasir Taib
Accurate classification of Aquilaria oil species is essential for ensuring the quality and authenticity of agarwood oils, which are widely used in perfumes and traditional medicine. This study investigated the effectiveness of the k-nearest neighbours (k-NN) machine learning model for classifying Aquilaria oil species based on four significant chemical compounds: dihyro-βagarofuran, δ-guaiene, 10-epi-γ-eudesmol, and γ-eudesmol. The dataset comprised 480 samples of Aquilaria oil, which were analyzed using gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID). The k-NN model, with an optimal k-value of 10 and using euclidean distance as the distance metric, achieved 100% accuracy, sensitivity, specificity, and precision in both training and testing datasets. These results demonstrate the robustness of k-NN in species identification, highlighting the discriminative power of the selected compounds. This study verifies that the integration of chemical profiling with machine learning offers a scalable solution for accurate species identification in the essential oil industry. Future work could explore hybrid models and data expansion techniques to further enhance the classification performance in more complex environmental conditions.
Volume: 39
Issue: 1
Page: 178-189
Publish at: 2025-07-01

A novel (𝒏, 𝒏) multi-secret image sharing scheme harnessing RNA cryptography and 1-D group cellular automata

10.11591/ijeecs.v39.i1.pp700-709
Yasmin Abdul , Venkatesan Ramasamy , Gaverchand Kukaram
In the modern landscape, securing digital media is crucial, as digital images are increasingly disseminated through unsecured channels. Therefore, image encryption is widely employed, transforming visual data into an unreadable format to enhance image security and prevent unauthorized access. This paper proposes an efficient (𝑛, 𝑛) multi-secret image sharing (MSIS) scheme that leverages ribonucleic acid (RNA) cryptography and one-dimensional (1-D) group cellular automata (GCA) rules. The (𝑛, 𝑛) MSIS scheme encrypts 𝑛 images into 𝑛 distinct shares, necessitating all 𝑛 shares for decryption to accurately reconstruct the original 𝑛 images. Initially, a key image is generated using RNA cryptography, harnessing the extensive sequence variability and inherent complexity of RNA. This secret key is then used to encrypt 𝑛 images in the primary phase. In the secondary phase, pixel values are transformed through multiple processes, with randomness achieved by executing a key function derived from GCA, known for its reversible properties, computational efficiency, and robustness against cryptographic attacks. The proposed model, implemented in Python, is validated through experimental results, demonstrating its effectiveness in resisting a broad spectrum of attacks, including statistical, entropy, differential, and pixel parity analyses. These findings affirm the model's durability, security, and resilience, underscoring its superior performance compared to existing models.
Volume: 39
Issue: 1
Page: 700-709
Publish at: 2025-07-01

Torque ripple minimization and performance enhancement of switched reluctance motor for electric vehicle application

10.11591/ijeecs.v39.i1.pp70-78
Yogesh B. Mandake , Deepak S. Bankar , Amit L. Nehete
Switched reluctance motors (SRMs) are an attractive choice for electric vehicle (EV) applications but suffer from certain limitations, such as high torque ripple and acoustic noise. This paper presents ongoing research and development activity details to enhance the performance of SRMs for EV applications. The poor performance of a conventional SRM which is available in market with a rating of 8/6 poles, 48 V, 500 W, and 2,000 rpm is tested. A motor model of the same rating is developed using ANSYS Maxwell software. Motor performance parameters important for EV applications, such as efficiency, rated torque and torque ripple are compared with the conventional motor. One novel technique to reduce the torque ripple of SRM is discussed along with the results. Torque ripple of developed software model is reduced by 24.52% without a reduction in the efficiency and rated torque of the motor. The performance of the developed SRM software model is better compared to conventional SRMs available in the market. 2D and 3D models of SRM were presented using ANSYS Maxwell software.
Volume: 39
Issue: 1
Page: 70-78
Publish at: 2025-07-01

An efficient segmentation using adaptive radial basis function neural network for tomato and mango plant leaf images

10.11591/ijeecs.v39.i1.pp202-213
Jolakula Asoka Smitha , Bichagal Shadaksharappa , Sheela Parvathy , Kilingar Veena , Albert Jenifer , Baddala Vijaya Nirmala , Subbiah Murugan
Agriculture has become simply to feed ever-growing populations. The tomato is arguably the most well-known vegetable in agricultural areas and plays a significant role in the growth of vegetables in our daily lives. However, because this tomato has multiple diseases, image segmentation of the diseased leaf shows a key role in classifying the disease by the leaf's symptoms. Therefore, in this paper, an efficient plant disease segmentation using an adaptive radial basis function neural network (ARBFNN) classifier. The proposed radial basis function (RBF) neural network is enhanced by using the flower pollination algorithm (FPA). Firstly, the noise is detached by an adaptive median filter and histogram equalization. Then, from every leaf image, different kind of color features is extracted. After the extraction of features, those are fed to the segmentation phase to section the disease serving from the input image. The efficiency of the suggested method is analyzed based on various metrics and our technique attained a better accuracy of 97.58%.
Volume: 39
Issue: 1
Page: 202-213
Publish at: 2025-07-01

Virtual learning environment on satisfaction and academic performance of students in institutions of higher learning

10.11591/ijeecs.v39.i1.pp258-271
Odunayo Dauda Olanloye , Peter Adebayo Idowu , Abidemi Emmanuel Adeniyi , Afolake Afusat Badmus , Oluwasegun Julius Aroba
As a result of the COVID-19 outbreak in 2020, education institutions across the world had to come to a functional standstill since they had to protect their students from viral exposures thereby affecting academic activities. However, several institutions had to adopt online virtual learning environments (VLE) using basic information and communication technology tools to provide platforms for teaching and learning thereby mitigating the effects of the pandemic on the students. This study was focused on the identification of the various types of VLE tools that were adopted alongside the impact that these tools had on learning satisfaction and the academic performance of students of higher learning in Nigeria. This study adopted a purposive simple random selection of undergraduate students of the department of computer science who had adopted the use of VLE to learn during the period of the pandemic. The results of the study showed that the most popular VLE tools were Zoom, Google Classroom, WhatsApp, Telegram, Coursera, Google Forms and learning management systems (LMS) while the least popular VLE tools were Microsoft Teams, Moodle/Edmondo, and Google Meet. The results showed that the students agreed to their behavioral intention to use VLE, the impact of VLE on learning satisfaction, and the impact of VLE on academic performance alongside the existence of a positive correlation among the research variables.
Volume: 39
Issue: 1
Page: 258-271
Publish at: 2025-07-01
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