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Eco-friendly durable asphalt using maleic-modified rosin ester

10.11591/ijaas.v14.i3.pp793-803
Emma Savitri , Edy Purwanto , Restu Kartiko Wisi , Aloisiyus Yuli Widianto , Reyhan Sava Pratama , Yosafat Gary Tegar Harijono
Asphalt, a crucial component of transportation infrastructure, particularly in regions with high traffic loads and extreme climates, often lacks the necessary elasticity, strength, and durability. Various asphalt modifiers have been explored, but many struggle with cost, thermal stability, and environmental impact. This study, however, investigates maleic-modified rosin ester, a gum rosin derivative, as a sustainable and cost-effective asphalt modifier. The base asphalt was heated to 150-190 °C, sheared at 100 rpm, and combined with 4-20% maleic rosin ester and sulfur. The modified asphalt was subjected to tests, including penetration, softening point, ductility, density, kinematic viscosity, Fourier transform infrared (FTIR), and dynamic shear rheometer (DSR) tests. The results are promising, showing that maleic rosin ester enhances penetration resistance and softening points while maintaining ductility and viscosity within acceptable limits. Chemical analysis confirmed improved adhesion, crosslinking, and thermal stability, making the modified asphalt more deformation-resistant. This suggests that maleic-modified rosin ester is a viable alternative to synthetic polymers, offering improved durability and sustainability. The enhanced durability of the modified asphalt provides confidence in its long-term performance, making it a reliable choice for transportation infrastructure.
Volume: 14
Issue: 3
Page: 793-803
Publish at: 2025-09-01

Combination of MLF-VO-F and loss functions for VOE from RGB image sequence using deep learning

10.11591/ijeecs.v39.i3.pp1571-1586
Van-Hung Le , Huu-Son Do , Thi-Ha-Phuong Nguyen , Van-Thuan Nguyen , Tat-Hung Do
Visual odometry estimation (VOE) is important in building navigation and pathfinding systems. It helps entities find their way and estimate paths in the environment. Most of the computer vision (CV)-based VOE models are usually evaluated and compared on the KITTI dataset. Multi-layer fusion framework (MLF-VO-F) has had good VOE results from red, green, and blue (RGB) image sequence in Jiang et al. study, using the DeepNet to extract the low-level textures, edges, and deeper high-level semantic features for estimating motion between consecutive frames. This paper proposed a combined model of MLFVO-F as a backbone and loss functions (LFs) (LMSE, LMSE−L2, LCE, and Lcombi) to optimize and supervise the training process of the VOE model. We evaluated and compared the effectiveness of LFs for VOE based on the KITTI and TQU-SLAM datasets with the original MLF-VO-F. From there, choose the appropriate LF combined with the backbone for VOE. The evaluation results on the KITTI dataset show that LCE(RT E is 0.075m, 0.06m on the Seq. #9, Seq. #10, respectively), and Lcombi (trel is 2.21%, 2.67%, 3.59%, 1.01%, and 4.62% on the Seq. #4, Seq. #5, Seq. #6, Seq. #7, Seq. #10, respectively) have the lowest errors and LMSE has the highest errors (AT E is 133.36m on the Seq. #9).
Volume: 39
Issue: 3
Page: 1571-1586
Publish at: 2025-09-01

Therapeutic potential of alpha-linolenic acid from Sacha Inchi oil in cervical cancer: an in vitro study on HeLa cells

10.11591/ijaas.v14.i3.pp966-974
Adi Permadi , Mutiara Wilson Putri , Muhammad Ali Akbar
This study investigated the potential of alpha-linolenic acid (ALA) from Sacha Inchi oil as a therapeutic agent for cervical cancer through an in vitro study on HeLa cells. Cervical cancer is one of the most common types of cancer in women, which is often caused by human papillomavirus (HPV) infection. Although chemotherapy therapy is one of the main methods in cancer treatment, this approach often causes side effects and drug resistance. ALA, which is one of the main components of Sacha Inchi oil, is known to have antioxidant and anti-cancer activities. In this study, Sacha Inchi oil was analyzed using liquid chromatography-high resolution mass spectrometry (LC-HRMS) for identification of its active components. Cytotoxic assays were performed using the MTT method on HeLa cells, which showed that ALA significantly inhibited cancer cell viability at low concentrations, with low IC50 values compared to the positive control compound cisplatin. These results suggest that ALA has potential as an effective anti-cancer agent against cervical cancer cells. This study concludes that ALA from Sacha Inchi oil can be a strong candidate in the development of safer and more effective cervical cancer therapy.
Volume: 14
Issue: 3
Page: 966-974
Publish at: 2025-09-01

Optimizing supervised learning model for thermal comfort and air quality

10.11591/ijeecs.v39.i3.pp1795-1806
Hidayatus Sibyan , Hermawan Hermawan , Ely Nurhidayati
Thermal comfort and indoor air quality are essential factors that directly influence occupants’ health and activity efficiency. Ensuring optimal thermal conditions also supports energy-efficient buildings by preventing energy waste. Machine learning models have been extensively applied to classify thermal comfort and air quality, with supervised learning algorithms such as support vector machine (SVM) and K-nearest neighbor (KNN) showing high accuracy. However, no prior study has compared or combined these two models for simultaneous prediction of thermal comfort and air quality, especially in diverse geographical settings. This study aims to develop and compare SVM and KNN to determine the most accurate model for enhancing thermal comfort and air quality in highland and lowland Islamic boarding schools. Using a quantitative approach, we collected datasets from schools in Wonosobo (highland) and Pontianak (lowland). The results show that KNN outperforms SVM in accuracy, precision, and F1-score. Additionally, a hybrid model integrating both algorithms further improves accuracy, achieving 91%. These findings highlight the effectiveness of machine learning in optimizing environmental conditions in educational settings.
Volume: 39
Issue: 3
Page: 1795-1806
Publish at: 2025-09-01

CNN-GRU based cyber-attack classification and detection with the CICIDS-2017 dataset using optimization algorithm for honey badger

10.11591/ijeecs.v39.i3.pp1765-1775
Katikam Mahesh , Kunjam Nageswara Rao
The sheer volume of data exchanged has grown through information and communications technology (ICT) swiftly growing importance since the attackers benefit from illegal access to network data and introduce possible dangers for data theft or alteration. It is considered a significant barrier to monitor the network traffic for cyber-attack detection and classification with alarm ring to inform to network administrator. With KDD-CUP99, conventional machine learning methods like deep neural network (DNN), a kind of artificial neural network (ANN), cannot detect and classify novel attacks types and lacks clarity regarding accuracy. The CICIDS 2017 dataset, which is improved in this study, serves as training data for the model and useful framework that combines a hybrid convolutional neural network (CNN) with the gated recurrent unit (GRU) technique. The primary aim of this effort is to classify different security attacks and classify cyberthreats with honey badger optimization algorithm (HBOA). To strengthen the performance criteria for various assault types, such as F1-score, recall, precision, and others, the HBOA is utilized to modify the model parameters high-level features ought to be extracted from the network data using the hybrid model assessed and verified by simulation studies. The detection and classification output from the CNN-GRU model, which detects different security threats with greater accuracy of 94%.
Volume: 39
Issue: 3
Page: 1765-1775
Publish at: 2025-09-01

Modern research of using alternative energy resources in Azerbaijan

10.11591/ijaas.v14.i3.pp907-915
Ramil Sadigov Ali , Mushkunaz Nazarova Kichmirza , Garayeva Irada Eyvaz , Gunay Mammadova Israphil , Turkan Hasanova Allahverdi , Muhammad Madnee
The article provides a comprehensive analysis of modern trends and prospects for the use of solar batteries in various sectors of the economy and the agricultural sector. The purpose of this article is to analyze the possibility of energy saving for a private residential building in Gobustan using solar energy storage in a greenhouse extension and a heat pump to transfer heat to the heating system. The calculation showed that in the coldest month, December, the potential of solar thermal energy is 15-38% of the required heat demand, depending on the material used in the extension design. In March and April, excess heat is generated, which can be used for hot water supply needs. Thus, for an individual residential building, the use of solar heat accumulated in a greenhouse extension is relevant as an additional source of heat for the heating system. Surface density of solar radiation flux, W/m2: surface density of direct solar radiation flux: 1,680 (November), 1,530 (December), 1,870 (January), 2,730 (February), 3,270 (March), 3,180 (April); Surface density of diffuse solar radiation flux: 650 (November), 450 (December), 480 (January), 680 (February), 1180 (March), 1,830 (April).
Volume: 14
Issue: 3
Page: 907-915
Publish at: 2025-09-01

Performance evaluation of multicarrier quadrature phase shift keying-based system under noisy channel conditions

10.11591/ijaas.v14.i3.pp693-701
Deepa Narayana Reddy , Aishwarya Nagaraju , Deepti Hosakere Prabhakara , Deekshitha Beeraganahalli Srinivas , Gandlaparthi Navyatha
A comprehensive analysis of quadrature phase shift keying (QPSK) modulation in both single input single output (SISO) and multiple input multiple output (MIMO) systems is conducted using MATLAB. The investigation focuses on evaluating QPSK performance with metrics such as signal-to-noise ratio (SNR) and bit error rate (BER) across diverse channel conditions. Furthermore, the study extends to encompass the integration of QPSK with orthogonal frequency division multiplexing (OFDM), with a particular emphasis on assessing spectral efficiency and error rate implications. To validate the accuracy of the simulations, QPSK and QPSK-OFDM configurations are implemented on the WiComm-T hardware platform, enabling a direct comparison of real-world performance metrics against simulation results. By offering practical insights and recommendations for the deployment of robust communication systems, this research underscores the inherent advantages of integrating OFDM with QPSK across both SISO and MIMO configurations.
Volume: 14
Issue: 3
Page: 693-701
Publish at: 2025-09-01

Photovoltaic energy harvesting for the power supply of medical devices

10.11591/ijpeds.v16.i3.pp1962-1969
Hamza Abu Owida , Basem Abu Izneid , Nidal Turab
The increasing demand for sustainable and reliable power sources in portable and implantable medical devices has led to growing interest in photovoltaic (PV) energy harvesting. Traditional power sources, such as batteries, are limited by finite energy capacity and frequent replacement or recharging needs, particularly in implantable devices where surgical intervention is required for battery replacement. Photovoltaic energy harvesting, which converts light into electrical energy, offers a promising alternative, especially in environments with consistent light exposure. This review provides an in-depth analysis of the advancements in PV technologies for powering medical devices. It covers various types of PV materials, design innovations, and the integration of energy storage systems. Additionally, the review highlights the application of PV systems in both external and implantable medical devices, while addressing critical challenges such as ensuring biocompatibility, optimizing performance in low-light conditions, and miniaturizing PV systems for implantation. The potential of PV energy harvesting to improve device longevity and reduce the need for invasive procedures is emphasized. This review concludes by outlining the current challenges and future directions needed to achieve widespread clinical adoption, aiming to contribute to the development of sustainable power solutions in healthcare.
Volume: 16
Issue: 3
Page: 1962-1969
Publish at: 2025-09-01

Self-development moderates the impact of digital literacy and talent on human error

10.11591/ijaas.v14.i3.pp682-692
Achmad Mirza , Isnurhadi Isnurhadi , Muhammad Ichsan Hadjri
Effective public services are important for increasing community satisfaction and organizational credibility. This study aims to explore the influence of digital literacy, underutilized talent, and human error on the effectiveness of public services, with self-development as a moderating variable. This study was conducted with employees of the Trade Office of South Sumatra Province. The research method used was quantitative data analysis, which was performed using partial least squares structural equation modeling (PLS-SEM). The results of this study show that digital literacy and self-development play an important role in reducing human error and increasing the effectiveness of public services. These findings have practical implications for human resource management in the public sector, focusing on improving digital literacy and employee self-development. 
Volume: 14
Issue: 3
Page: 682-692
Publish at: 2025-09-01

Mechanized network based cyber-attack detection and classification using DNN-generative adversarial model

10.11591/ijeecs.v39.i3.pp1755-1764
Katikam Mahesh , Kunjam Nageswara Rao
These days almost everything is internet. Cyberattacks are the world's most pressing issues. Due to these attacks, Computer systems can be rendered inoperable, disrupted, destroyed or controlled via cyberattacks. Additionally, they can be used to steal, modify, erase, block, or alter data. Most organizations are facing this Issue and lose financially as well as in data security, there are numerous conventional intrusion detection systems (IDS) and firewalls are illustrations for network security tools which are not able to classify and detect different types of attacks in network. With machine learning approach using the Dataset KDD_CUP 99 as input, the synthetic minority oversampling technique (SMOTE) is one of the most often used oversampling methods for addressing imbalance issues. The proposed hybrid deep neural network (DNN), generative adversarial network (GAN), and exhaustive feature selection (EFS) can detect and classify several attack types including R2L, U2R, Probe, denial of service (DoS), and normal attacks types and inform to administrator to ring alarm sound to control and monitor network traffic in dynamically typed networks.
Volume: 39
Issue: 3
Page: 1755-1764
Publish at: 2025-09-01

Prediction of Parkinson's disease using feature selection and ensemble learning techniques

10.11591/ijeecs.v39.i3.pp1736-1744
Sharan T. D. , Sujata Joshi
Parkinson's disease (PD) is a progressive neurodegenerative disorder that significantly impacts quality of life and healthcare systems. Early detection is crucial for timely interventions that can mitigate disease progression and improve patient outcomes. This study leverages advanced machine learning (ML) techniques to detect PD using speech features as non-invasive biomarkers. A dataset containing 754 features derived from sustained vowel phonations of 252 individuals (188 PD patients, 64 healthy controls) was analyzed. The dataset, originally collected by Istanbul University and publicly hosted via the UCI ML repository, was accessed through Kaggle for preprocessing and analysis. To identify the most predictive features, we employed recursive feature elimination (RFE), random forest importance, lasso regression, and the boruta algorithm—ensuring robust feature selection while reducing dimensionality. The XGBoost model, optimised using synthetic minority oversampling technique (SMOTE) for class balancing, achieved an accuracy of 96.69%, a recall of 96%, and an F1-score of 98%. Model robustness was validated through 5-fold cross-validation, yielding an average accuracy of 89.54%. These findings establish a scalable, costeffective, and non-invasive framework for early PD detection, demonstrating the potential of speech analysis and ML in neurodegenerative disease management.
Volume: 39
Issue: 3
Page: 1736-1744
Publish at: 2025-09-01

Analysis of mobile banking adoption in Ghana: do education levels differ?

10.11591/ijaas.v14.i3.pp828-837
Isaac Asampana , Lawrence Kwami Aziale , Henry Matey Akwetey , Hannah Ayaba Tanye
This study investigates the role of educational attainment in mobile banking (m-banking) adoption in Ghana, leveraging data from 598 respondents through a multi-group analysis. By integrating the technology acceptance model (TAM) and the theory of planned behavior (TPB) into a structural equation modelling framework, the research examines key factors such as subjective norms, perceived usefulness, ease of use, trust, and self-efficacy. Results reveal significant differences in adoption behaviors between lower- and higher-educated users. Subjective norms strongly influence higher-educated individuals, while perceived ease of use drives adoption among lower-educated users. Perceived usefulness positively affects higher-educated users but has a negative impact on lower-educated respondents. The findings highlight the moderating effect of education level on the adoption process, offering theoretical and practical insights into targeted strategies for enhancing financial inclusion in developing economies. These results underscore the importance of user segmentation in fostering broader acceptance and utilization of m-banking technologies.
Volume: 14
Issue: 3
Page: 828-837
Publish at: 2025-09-01

Redesign the layout of the raw material warehouse from randomized storage to class-based storage

10.11591/ijaas.v14.i3.pp773-783
Nur Iftitah , Qurtubi Qurtubi , Danang Setiawan , Vembri Noor Helia
The company has a problem of ineffectiveness in the layout of the raw material warehouse due to the use of storage methods that ignore factors such as the type, dimensions, and condition of the goods. This reduces the optimal function of the warehouse and increases the time to retrieve goods. This research aims to redesign the suitable and practical layout of the raw material warehouse by considering its form and function, as well as filling methodological gaps from previous research. The method used is class-based storage. Based on ABC analysis, the category with the highest value is class C goods, with 73 units. Meanwhile, from the fast, slow, non-moving (FSN) analysis, class F (fast-moving) goods have the highest frequency of movement, with a movement percentage of 63% for 10 units of goods. The warehouse slotting analysis shows an increase in the number of shelves from nine to 15 shelves with five different shelf models and layout changes in raw material warehouses 1 and 2. The class-based storage method results in a more organized layout, efficient movement of goods, and faster picking time to optimize warehouse functions.
Volume: 14
Issue: 3
Page: 773-783
Publish at: 2025-09-01

Predictive machine learning for smart grid demand response and efficiency optimization

10.11591/ijpeds.v16.i3.pp1628-1636
J. C. Vinitha , J. Sumithra , M. J. Suganya , P. Aileen Sonia Dhas , Balaji Ramalingam , Sivakumar Pushparaj
This paper explores the evolution of smart grids (SGs) and how they enable consumers to schedule household appliances based on demand response programs (DRs) provided by distribution system operators (DSOs). This study looks at and compares four distinct regression models: linear regression, random forest regressor, gradient boosting regressor, and support vector regressor. This is being done because more and more people are using machine learning (ML) methods to make this process better. The models are trained and tested using a dataset that includes a variety of parameters, such as humidity, temperature, and the amount of power used by appliances. Mean squared error (MSE) and R-squared values are two important performance measures that are used to judge these models and see how well they can make predictions. These results reveal that the gradient boosting regressor was the most accurate model for figuring out how much energy smart homes use. This algorithm could be a great tool for better managing energy use because it can figure out the complicated connections between the things that are input and the amount of energy that appliances use. This study makes a big difference in the creation of strong regression models by emphasizing how important it is to be accurate when making predictions. This, in turn, helps to enhance energy sustainability and economic stability in smart home environments.
Volume: 16
Issue: 3
Page: 1628-1636
Publish at: 2025-09-01

Optimizing energy efficiency and improved security in wireless sensor networks using energy-centric MJSO and MACO for clustering and routing

10.11591/ijeecs.v39.i3.pp1964-1975
Srinivas Kalaskar , Channappa Bhyri
Wireless sensor networks (WSNs) play a pivotal role in various applications, but their energy-constrained nature poses significant challenges to their sustainable operation. In this paper, we propose a novel approach to enhance energy efficiency in WSNs by leveraging energy-centric multi-objective jaya search optimization (MJSO) and multi-objective ant colony optimization (MACO) for clustering and routing. Our method aims to address the energy consumption issues by optimizing clustering and routing strategies simultaneously. The energy-centric MJSO algorithm is employed to intelligently organize sensor nodes into clusters, considering energy consumption, network coverage, and connectivity. The multi-objective MACO algorithm optimizes routing paths by balancing energy consumption and network lifetime objectives. Through integration and simulations, the approach enhances energy efficiency in WSNs for various applications like environmental monitoring and smart cities, advancing energy-efficient clustering and routing. By integrating energy-centric MJSO and MACO into clustering and routing protocols, WSNs can achieve significant improvements in energy efficiency and security while maintaining reliable communication and data delivery.
Volume: 39
Issue: 3
Page: 1964-1975
Publish at: 2025-09-01
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