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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

A comparative analysis of hybrid of traditional load flow methods for IEEE distributed power generation networks

10.11591/ijeecs.v39.i1.pp33-44
Muhammad Hafeez Mohamed Hariri , Noor Dzulaikha Daud , Nor Azizah Mohd Yusoff , Syed Muhammad Zakwan Syed Zaman , Mohd Khairunaz Mat Desa
Analyzing power flow or load flow is crucial for planning, operating, maintaining, and controlling electrical power systems. Two traditional power flow methods namely the Newton-Raphson (NR) method are known for their accuracy and robustness nevertheless high computational intensity, and the fast decoupled load flow (FD) method, is valued for its computational efficiency and speed, however, generating less accurate data. This research aims to develop a hybrid load flow technique that integrates both strengths, achieving higher accuracy and faster convergence. The validation processes are based on several IEEE standard bus systems, including the 3-bus, 9-bus, 14-bus, and 30-bus systems. These systems, with different bus types and interconnections, represent real-world operations and help generate comprehensive data on iteration count, execution time, and the accuracy of the output data results. A new hybrid method generated from this research work compared to traditional load flow methods, provides a substantially well-balanced number of iteration counts, the fastest execution times, improved by 41.55%, and produces a similar accuracy of the data set. These improvements make the hybrid method highly advantageous in practical real-time applications and large-scale systems where both accuracy and speed are critical.
Volume: 39
Issue: 1
Page: 33-44
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

Blockchain technology for optimizing security and privacy in distributed systems

10.11591/csit.v6i2.p210-220
Wisnu Uriawan , Adrian Putra Pratama , Shafwan Mursyid
Blockchain technology is increasingly recognized as an effective solution for addressing security and privacy challenges in distributed systems. Blockchain ensures information security by validating data and defending against cyber threats, while guaranteeing data integrity through transaction validation and reliable storage. The research involves a literature study, problem identification, analysis of blockchain security and privacy, model development, testing, and analysis of trial results. Furthermore, blockchain enables user anonymity and fosters transparency by utilizing a distributed network, reducing the risk of fraudulent activities. Its decentralized nature ensures high reliability and accessibility, even in node failures. Blockchain enhances security and privacy by offering features like data immutability, provenance, and reduced reliance on trust. It decentralizes data storage, making tampering or deletion extremely challenging, and ensures the invalidation of subsequent blocks upon any changes. Blockchain finds applications in various domains, including supply chains, finance, healthcare, and government, enabling enhanced security by tracking data origin and ownership. Despite scalability and security challenges, the potential benefits of reduced costs, increased efficiency, and improved transparency position blockchain as a promising technology for the future. In summary, blockchain technology provides secure transaction recording and data storage, thus enhancing security, privacy, and the integrity of sensitive information in distributed systems.
Volume: 6
Issue: 2
Page: 210-220
Publish at: 2025-07-01

Study on neuromorphic computation and its applications

10.11591/ijeecs.v39.i1.pp272-282
Anjali Chature , A. Raganna , Venkateshappa Venkateshappa
Neuromorphic computing offers a promising alternative to traditional von Neumann architectures, especially for applications that require efficient processing in edge environments. The challenge lies in optimizing spiking neural networks (SNNs) for these environments to achieve high computational efficiency, particularly in event-driven applications. This paper investigates the integration of advanced simulation tools, such as Simeuro and SuperNeuro, to enhance SNN performance on edge devices. Through comprehensive studies of various SNN models, a novel SNN design with optimized hardware components is proposed, focusing on energy and communication efficiency. The results demonstrate significant improvements in computational efficiency and performance, validating the potential of neuromorphic architectures for executing event-driven scientific applications. The findings suggest that neuromorphic computing can transform the way edge devices handle event-driven tasks, offering a pathway for future innovations in diverse application domains.
Volume: 39
Issue: 1
Page: 272-282
Publish at: 2025-07-01

An ensemble learning approach for diabetes prediction using the stacking method

10.11591/csit.v6i2.p102-111
Elliot Kojo Attipoe , Alimatu Saadia Yussiff , Maame Gyamfua Asante-Mensah , Emmanuel Dortey Tetteh , Regina Esi Turkson
Diabetes is a severe illness characterized by high blood glucose levels. Machine learning algorithms, with their ability to detect and predict diabetes in its early stages, offer a promising avenue for research. This study sought to enhance the accuracy of predicting diabetes mellitus by employing the stacking method. The stacking method was chosen because it integrates predictions from various base models, resulting in a more precise final prediction. The stacking method enhances accuracy and generalization by utilizing the varied strengths of multiple base models. The Pima Indians diabetes dataset, a widely used benchmark dataset, was utilized in the study. The machine learning models used for the studies were logistic regression (LR), naïve Bayes (NB), extreme gradient boost (XGBoost), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM). LR, KNN, and SVM were the best-performing models based on accuracy, F1-score, precision, and area under the curve (AUC) score, and were consequently used as the base model for the stacking method. The LR model was utilized for the meta-model. The proposed ensemble approach using the stacking method demonstrated a high accuracy of 82.4%, better than the individual models and other ensemble techniques such as bagging or boosting. This study advances diabetes prediction by developing a more accurate early-stage detection model, thereby improving clinical management of the disease.
Volume: 6
Issue: 2
Page: 102-111
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

Bibliometric analysis and short survey in CT scan image segmentation: identifying ischemic stroke lesion areas

10.11591/csit.v6i2.p91-101
Wahabou K. Taba Chabi , Sèmèvo Arnaud R. M. Ahouandjinou , Manhougbé Probus A. F. Kiki , Adoté François-Xavier Ametepe
Ischemic stroke remains one of the leading causes of mortality and long-term disability worldwide. Accurate segmentation of brain lesions plays a crucial role in ensuring reliable diagnosis and effective treatment planning, both of which are essential for improving clinical outcomes. This paper presents a bibliometric analysis and a concise review of medical image segmentation techniques applied to ischemic stroke lesions, with a focus on tomographic imaging data. A total of 2,014 publications from the Scopus database (2013–2023) were analyzed. Sixty key studies were selected for in-depth examination: 59.9% were journal articles, 29.9% were conference proceedings, and 4.7% were conference reviews. The year 2023 marked the highest volume of publications, representing 17% of the total. The most active countries in this area of research are China, the United States, and India. "Image segmentation" emerged as the most frequently used keyword. The top-performing studies predominantly used pre-trained deep learning models such as U-Net, ResNet, and various convolutional neural networks (CNNs), achieving high accuracy. Overall, the findings show that image segmentation has been widely adopted in stroke research for early detection of clinical signs and post-stroke evaluation, delivering promising outcomes. This study provides an up-to-date synthesis of impactful research, highlighting global trends and recent advancements in ischemic stroke medical image segmentation.
Volume: 6
Issue: 2
Page: 91-101
Publish at: 2025-07-01

Web-Based Attacks Detection Using Deep Learning Techniques: A Comprehensive Review

10.11591/ijeecs.v39.i1.pp466-484
Lujain Nasser Alghofaili , Dina M. Ibrahim
Web applications are utilized extensively by a broad user base, and the services provided by these applications assist enterprises in enhancing the quality of their service operations as well as increasing their revenue or resources. To gain control of web servers, attackers will frequently attempt to modify the web requests that are sent by users from web applications. Attacks that are based on the web can be detected to help avoid the manipulation of web applications. In addition, a variety of research has offered many methods, one of which is artificial intelligence (AI), which is the method that has been utilized the most frequently to identify web-based attacks recently. When it comes to the protection of web applications, anomaly detection techniques used by intrusion prevention systems are preferred.  Deep learning, often known as DL, is going to be covered in this paper as anomaly-based web attack detection methods and machine learning techniques. With the purpose of organizing our selected techniques into a comprehensive framework that encourages future studies, we first explained the most concepts that related to web-based attacks detection, then we moved on to discuss the most prevalent web risks and may provide inherent difficulties for keeping web applications safe.  We classify previous studies on detecting web attacks into two categories: deep learning and machine learning. Lastly, we go over the features of the previously utilized datasets in summary form.
Volume: 39
Issue: 1
Page: 466-484
Publish at: 2025-07-01

Comparative study of deep learning approaches for cucumber disease classification

10.11591/ijeecs.v39.i1.pp554-563
Supreetha Shivaraj , Manjula Sunkadakatte Haladappa
Cucumber leaf diseases, such as downy mildew and leaf miner, pose significant challenges to crop yield and quality. Accurate and timely detection is essential to efficient management. The current research assesses seven convolutional neural network (CNN) models for the classification of diseases of cucumber leaves: DenseNet121, InceptionV3, ResNet50V2, VGG16, Xception, MobileNetV2, and NASNet. The dataset includes images from the cucumber disease recognition dataset (Mendeley) and 500 real-time images captured between December 2022 and February 2023 in Karnataka, covering varied lighting conditions. After augmentation, the dataset is divided into testing, validation, and training sets and includes 804 leaf miner, 807 downy mildew, and 804 healthy images. With an overall test accuracy of 99.37% and nearly flawless precision, recall, and F1-scores in every class, ResNet50V2 showed exceptional performance. InceptionV3 and MobileNetV2 also exhibited strong performance with accuracies of 97.29% and 97.70%, respectively. DenseNet121, VGG16, Xception, and NASNet performed well but were slightly outperformed by the top models. The findings indicate ResNet50V2 as the most reliable model for cucumber leaf disease classification, providing a robust foundation for developing automated disease detection systems. This work demonstrates how precise disease detection using deep learning models can improve agricultural management.
Volume: 39
Issue: 1
Page: 554-563
Publish at: 2025-07-01

OPT-TMS: a transport management system based on unsupervised clustering algorithms

10.11591/ijeecs.v39.i1.pp425-435
Soufiane Reguemali , Abdellatif Moussaid , Abdelmajid Elaoudi
Transportation management within modern logistics has become increasingly complex, particularly with the expansion of industrial zones outside urban centers. This paper introduces OPT-TMS, a cutting-edge transportation management system (TMS) designed to optimize employee transportation using advanced machine learning techniques, specifically unsupervised learning and clustering algorithms. OPT-TMS integrates a comprehensive dataset that includes employee locations, entry times, bus capacities, and other critical parameters to enhance resource utilization, reduce costs, and improve overall efficiency. The proposed system follows a systematic workflow encompassing data collection, preparation, and adaptive clustering using the K-means algorithm with constraints. The innovative approach leverages real-time data integration through the open route services (ORS) API to optimize bus routes and collection points. Extensive validation, involving both data verification and physical testing, confirms the system’s accuracy and effectiveness across multiple Moroccan cities, including Casablanca, Kenitra, and Marrakech. The development of OPT-TMS into a user-friendly web application further demonstrates its practical utility, offering decision-makers a dynamic tool for real-time adjustments and efficient transportation management. This paper concludes that OPT-TMS represents a significant advancement in transportation logistics, enhancing both employee satisfaction and operational efficiency through data-driven optimization.
Volume: 39
Issue: 1
Page: 425-435
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

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

Effects of hyperparameter tuning on random forest regressor in the beef quality prediction model

10.11591/csit.v6i2.p159-166
Ridwan Raafi'udin , Yohanes Aris Purwanto , Imas Sukaesih Sitanggang , Dewi Apri Astuti
Prediction models for beef meat quality are necessary because production and consumption were significant and increasing yearly. This study aims to create a prediction model for beef freshness quality using the random forest regressor (RFR) algorithm and to improve the accuracy of the predictions using hyperparameter tuning. The use of near-infrared spectroscopy (NIRS) in predicting beef quality is an easy, cheap, and fast technique. This study used six meat quality parameters as prediction target variables for the test. The R² metric was used to evaluate the prediction results and compare the performance of the RFR with default parameters versus the RFR with hyperparameter tuning (RandomSearchCV). Using default parameters, the R-squared (R²) values for color (L*), drip loss (%), pH, storage time (hour), total plate colony (TPC in cfu/g), and water moisture (%) were 0.789, 0.839, 0.734, 0.909, 0.845, and 0.544, respectively. After applying hyperparameter tuning, these R² scores increased to 0.885, 0.931, 0.843, 0.957, 0.903, and 0.739, indicating an overall improvement in the model’s performance. The average performance increase for prediction results for all beef quality parameters is 0.0997 or 14% higher than the default parameters.
Volume: 6
Issue: 2
Page: 159-166
Publish at: 2025-07-01

HepatoScan: Ensemble classification learning models for liver cancer disease detection

10.11591/csit.v6i2.p167-175
Tella Sumallika , Raavi Satya Prasad
Liver cancer is a dangerous disease that poses significant risks to human health. The complexity of early detection of liver cancer increases due to the unpredictable growth of cancer cells. This paper introduces HepatoScan, an ensemble classification to detect and diagnose liver cancer tumors from liver cancer datasets. The proposed HepatoScan is the integrated approach that classifies the three types of liver cancers: hepatocellular carcinoma, cholangiocarcinoma, and angiosarcoma. In the initial stage, liver cancer starts in the liver, while the second stage spreads from the liver to other parts of the body. Deep learning is an emerging domain that develops advanced learning models to detect and diagnose liver cancers in the early stages. We train the pre-trained model InceptionV3 on liver cancer datasets to identify advanced patterns associated with cancer tumors or cells. For accurate segmentation and classification of liver lesions in computed tomography (CT) scans, the ensemble multi-class classification (EMCC) combines U-Net and mask region-based convolutional network (R-CNN). In this context, researchers use the CT scan images from Kaggle to analyze the liver cancer tumors for experimental analysis. Finally, quantitative results show that the proposed approach obtained an improved disease detection rate with mean squared error (MSE)-11.34 and peak signal-to-noise ratio (PSNR)-10.34, which is high compared with existing models such as fuzzy C-means (FCM) and kernel fuzzy C-means (KFCM). The classification results obtained based on detection rate with accuracy-0.97%, specificity-0.99%, recall-0.99%, and F1S-0.97% are very high compared with other existing models.
Volume: 6
Issue: 2
Page: 167-175
Publish at: 2025-07-01
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