Articles

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

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

29,922 Article Results

Design and implementation of an internet of things-based automatic waste sorting system

10.11591/ijaas.v14.i4.pp1155-1165
Akhmad Taufik , Paisal Paisal , Muhammad Ruswandi Djalal , Zahran Atha Dillah , Haryono Ismail
This paper presents the design and development of an internet of things (IoT)-based automatic waste sorting system that classifies waste into four categories: organic, non-organic, metal, and others. The system integrates an Arduino Mega for control, multiple proximity sensors (inductive, capacitive, and infrared), and ultrasonic sensors for level detection, and a NodeMCU ESP8266 for real-time monitoring via the Blynk platform. A total of 100 tests (25 per bin) were conducted. Classification success rates were 92% (metal), 80% (inorganic), 84% (organic), and 100% (others), resulting in an overall accuracy of 89%. The main contribution is a combined automatic sorting and IoT monitoring framework suitable for campus-scale deployment.
Volume: 14
Issue: 4
Page: 1155-1165
Publish at: 2025-12-01

Greywater treatment system based on fuzzy logic control

10.11591/ijai.v14.i6.pp4643-4651
I Putu Eka Widya Pratama , Muhammad Rasyid Ridha , Anis Mahmuda Chafsah , Akhmad Ibnu Hija , Siti Nur Azella Zaine
Greywater from households and public facilities represents a major source of untreated wastewater, carrying high microbial loads and variable chemical composition that threaten environmental and public health. Conventional treatment systems often lack adaptive control mechanisms capable of handling the dynamic fluctuations of greywater quality. This study presents the design and validation of an intelligent greywater treatment system that integrates real–time sensing with a Sugeno fuzzy logic controller to regulate pump and solenoid valve operation. The system continuously monitors pH, total dissolved solids (TDS), dissolved oxygen (DO), and ammonia (NH3), and dynamically adjusts treatment cycles based on sensor feedback. Experimental deployment demonstrated significant improvements in effluent quality, with pH reduced from 9.04 to 8.08, TDS from 611.04 ppm to 393.96 ppm, and NH₃ from 0.52 ppm to 0.19 ppm, while DO increase from 2.52 mg/L to 6.07 mg/L. These results confirm that fuzzy logic–based control enhances system responsiveness and ensures effluent compliance under variable influent conditions. The proposed framework provides a scalable, cost-effective solution for decentralized wastewater management, advancing the development of intelligent treatment technologies for sustainable urban water systems.
Volume: 14
Issue: 6
Page: 4643-4651
Publish at: 2025-12-01

A two-step intelligent framework for gene expression-based cancer diagnosis

10.11591/ijai.v14.i6.pp4731-4738
Sara Haddou Bouazza , Jihad Haddou Bouazza
DNA microarray technology has advanced cancer diagnosis by enabling large-scale gene expression analysis, yet challenges remain in selecting relevant genes and achieving accurate classification. This study introduces two novel methods: the three-stage gene selection (3SGS) method and the statistics classifier (SC). By eliminating redundant, noisy, and less informative genes, the 3SGS method effectively lowers the dimensionality of gene expression data, while the SC classifier uses statistical measures of gene expression to classify samples with high accuracy and speed. Evaluated on leukemia, prostate cancer, and colon cancer datasets, the 3SGS method effectively identified minimal yet informative gene subsets, achieving 100% accuracy for leukemia, 99.3% for prostate cancer, and 97% for colon cancer. The SC classifier consistently outperformed traditional models in both accuracy and computational efficiency, completing predictions in under 2 seconds per dataset. Compared to conventional classifiers, it requires no parameter tuning and performs reliably even with small gene sets. While promising, future work should address multiclass classification and clinical validation to broaden the framework’s applicability. Together, these methods offer a precise and rapid cancer classification framework, supporting early diagnosis and personalized treatment strategies across diverse cancer types.
Volume: 14
Issue: 6
Page: 4731-4738
Publish at: 2025-12-01

Effect on saturated and unsaturated fatty acids on various vegetable oils on droplet combustion characteristic

10.11591/ijape.v14.i4.pp980-987
Dony Perdana , Muhamad Nur Rohman , Mochamad Choifin
Vegetable oils have composed of triglycerides, which one consist of 3 fatty acids combined with glycerol. Each saturated and unsaturated fatty acid has a different effect on burning characteristics. This study aimed to investigated effect of fatty acids at ceiba pentandra and jatropha oils on the flame behavior of the droplet combustion process. The combustion characteristic was observed by an ignited droplet at the junction using a thermocouple and a high-speed camera (120 fps). Results showed that a higher saturated fatty acid content resulted in long-life and steady flames. This is because more oleic and linoleic acid carbon atoms leave the droplet area and react with air. Jatropha oil produces a higher temperature of 780 °C than ceiba pentandra oil. Temperature of a vegetable oils flame is influenced by number of carbon chains, double bond, and heating value. Ceiba pentandra oil has a higher burning rate of 0.185 mm/s than jatropha oil at 0.155 mm/s. The chain content of polyunsaturated fatty acids has significant effect on rate of combustion, which is due to the weak van der Waals dispersion forces, such that heat absorption is more active and energetic. The highest flame height for ceiba pentandra oil is 55.03 mm compared to for jatropha oil it is 46.82 mm. Long-chain unsaturated double bonds and glycerol cause micro-explosions. This micro-explosion caused the shape of the flame to split and expand so that evaporation occurred faster, thus increasing the size of the flame.
Volume: 14
Issue: 4
Page: 980-987
Publish at: 2025-12-01

Implementation of a network intrusion detection system for man-in-the-middle attacks

10.11591/ijece.v15i6.pp3913-3927
Kennedy Okokpujie , William A. Abdulateef-Adoga , Oghenetega C. Owivri , Adaora P. Ijeh , Imhade P. Okokpujie , Morayo E. Awomoy
Intrusion detection systems (IDS) are critical tools designed to detect and prevent unauthorized access and potential network threats. While IDS is well-established in traditional wired networks, deploying them in wireless environments presents distinct challenges, including limited computational resources and complex infrastructure configurations. Packet sniffing and man-in-the-middle (MitM) attacks also pose significant threats, potentially compromising sensitive data and disrupting communication. Traditional security measures like firewalls may not be sufficient to detect these sophisticated attacks. This paper implements a network intrusion detection system that monitors a computer network to detect Address Resolution Protocol spoofing attacks in real-time. The system comprises three host machines forming the network. Using Kali Linux, a bash script is deployed to monitor the network for signs of address resolution protocol (ARP) poisoning. An email alert system is integrated into the bash script, running in the background as a service for the network administrator. Various ARP spoofing attack scenarios are performed on the network to evaluate the efficiency of the network IDS. Results indicate that deploying IDS as a background service ensures continuous protection against ARP spoofing and poisoning. This is crucial in dynamic network environments where threats may arise unexpectedly.
Volume: 15
Issue: 6
Page: 6027-6042
Publish at: 2025-12-01

Eco-friendly innovation: green energy empowered by IoT

10.11591/ijape.v14.i4.pp903-911
Nikita Amoli , Jitendra Singh , Rahul Mahala , Rajesh Singh , Anita Gehlot , Mahim Raj Gupta
Energy demand is high globally, impacting daily life and promoting sustainable modernization. Goal 9 aims to build an elastic framework for economies, while Goal 15 of the Sustainable Development Goals (SDGs) emphasizes the preservation of terrestrial environment, sustainable woodland management, and biodiversity conservation. The International Energy Agency predicts a significant increase in global renewable capacity, with solar PV being two-third of this growth. Green technology is crucial to combat global warming and Industry 4.0, a digital transformation that aims to create a strong framework for sustainable modernization. The growth of the smart grid is vital, involving energy sources, control techniques, computation, generation, transmission, distribution, and more. Supercapacitors store and deliver energy at high capacity, while green energy transforms fossil fuels into eco-friendly sources using natural resources like hydro, solar, wind, thermal, and biomass. This study explores the efficient use of microprocessors in solar and wind energy, as well as the application of actuators in the green energy sector. Green energy is a sustainable solution to increasing energy needs, reducing dependence on fossil fuels. IoT technologies, including sensors, actuators, microprocessors, and microcontrollers, are used in energy generation, transmission, distribution, and composition.
Volume: 14
Issue: 4
Page: 903-911
Publish at: 2025-12-01

Enhancing learning outcomes in smart education: a supervised machine learning predictive analytics model for course completion

10.11591/ijai.v14.i6.pp4711-4721
Abdellah Bakhouyi , Amine Dehbi , Lahcen Amhaimar , Yassine Tazouti , Younes Nadir , Abderrahim Khalidi
Predictive analytics have become increasingly capable of delivering actionable and accessible feedback to enhance teacher performance to enhance student outcomes in higher education. This study introduces a supervised machine learning predictive model designed to forecast the duration required to complete a course in a video learning environment using a dataset of 8,665 statements from 490 students from National Higher School of Art and Design at Hassan II University in Casablanca over six academic years (2019-24). This paper analyzes decision trees (DT), random forest (RF), support vector machines (SVM), gradient boosting (GB), and linear regression (LR) techniques. The CMI-5 standard and JSON format are used to automatically transfer learning activity data from the learning management system (LMS) to the learning record store (LRS). The results indicate that DT, RF, and GB achieved 100 percent predictor accuracy.
Volume: 14
Issue: 6
Page: 4711-4721
Publish at: 2025-12-01

Bidirectional AC/DC converter connecting AC and DC microgrids for smart grids

10.11591/ijpeds.v16.i4.pp2549-2561
Nguyen Van Dung , Nguyen The Vinh
This paper proposes a converter connecting two independent AC and DC microgrids in a flexible microgrid and smart grid system. With this converter, basic DC/DC converter types such as Flyback are used to develop the power circuit and controller for the converter that is capable of integrating the operating functions for the operation between microgrids. The converter uses bidirectional switching locking technology to simplify the control algorithm. The energy is converted in two directions, AC/DC and DC/AC, with different working principles of increasing and decreasing voltage according to the standards of the distribution grid and DC microgrid. The TDH value is significantly limited when using the recovery circuit solution. The converter is designed, simulated based on OrCAD software, and tested with a capacity in the range of 2-10 kW. The DC microgrid output voltage is 400 VDC, voltage is 220 VAC.
Volume: 16
Issue: 4
Page: 2549-2561
Publish at: 2025-12-01

Solving sparsity and scalability problems for book recommendations on e-commerce

10.11591/ijai.v14.i6.pp4865-4877
Muhammad Ichsanudin , Bevina Desjwiandra Handari , Bambang Dwi Wijanarko , Gatot Fatwanto Hertono
This study proposed a hierarchical density-based spatial clustering of applications with noise (HDBSCAN) and randomized singular value decomposition (RSVD) collaborative filtering (CF) method to overcome sparsity and scalability problems for book recommendations on e-commerce. CF is an information retrieval system that assumes a user has the same interest in an object as other users have in the past. When handling large volumes of data, sparsity problems can arise, where finding a similarity relation of user preferences results from a small assessment of an object by users. The scalability is the increased computation of an algorithm caused by increased users or objects, which makes recommendations take longer to form, therefore making them less accurate. HDBSCAN is a density-based clustering method that simplifies the hierarchical arrangement of the most significant clusters for extraction to group users in the same cluster. RSVD is a linear dimension reduction method that breaks a matrix into three sub matrices by reconstructing the size of that matrix without removing its dominant part, especially for cluster result matrices. The HDBSCAN RSVD-CF model reduced the root mean squared error (RMSE) by 21.83%, being 3793.73 seconds faster than the CF model. It also performed very well compared to both RSVD-CF and HDBSCAN-CF.
Volume: 14
Issue: 6
Page: 4865-4877
Publish at: 2025-12-01

Classifier model for lecturer evaluation by students using speech emotion recognition and deep learning approaches

10.11591/ijai.v14.i6.pp5157-5171
Yesy Diah Rosita , Wahyu Andi Saputra
Lecturers play a crucial role in higher education, with their teaching behavior directly impacting learning and teaching quality. Lecturer evaluation by students (LES) is a common method for assessing lecturer performance, though it often relies on subjective perceptions. As a more objective alternative, speech emotion recognition (SER) uses speech technology to analyze emotions in the speech of lecturers during classes. This study proposes using deep learning-based SER, including convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM), to evaluate teaching quality by analyzing displayed emotions. Removing silence from audio signals is crucial for enhancing feature analysis, such as energy, zero-crossing rate (ZCR), and mel-frequency cepstral coefficients (MFCC). This method removes inactive segments, emphasizing significant segments, and improving accuracy in detecting voice and emotions. Results show that the 1D CNN model with Bi-LSTM, using MFCC with 13 coefficients, energy, and ZCR, performs excellently in emotion detection, achieving a validation accuracy of over 0.851 with an accuracy gap of 0.002. This small gap indicates good generalization and reduces the risk of overfitting, making teaching evaluations more objective and valuable for improving practices.
Volume: 14
Issue: 6
Page: 5157-5171
Publish at: 2025-12-01

Integration and optimization of grid through ANN-based solar MPPT and battery

10.11591/ijape.v14.i4.pp988-998
Kolli Sujran , Ankala Sirisha , Ganapaneni Swapna , Malligunta Kiran Kumar , Kambhampati Venkata Govardhan Rao
Integration of solar energy into the grid is the most important aspect for achieving sustainable energy systems. This paper presents an artificial neural network-based maximum power point tracking (ANN-MPPT) system with battery storage to enhance grid efficiency. The proposed ANN-MPPT is dynamically adapted to the varying irradiance and temperature, hence ensuring optimal power extraction from the photovoltaic system. Excess energy is stored in batteries during high solar radiation and discharged when solar generation is low or grid demand is high, maintaining a stable power supply. This system enhances the grid performance in terms of supporting real-time energy exchange, load balancing, and grid stability. Efficient management of the energy fluctuations ensures reliability even at times of grid failures. Further, integration of ANN-based MPPT with battery storage reduces dependence on non-renewable sources and harmonizes solar energy utilization. It can be achieved through enabling smarter energy management and thus contributing to the resilience and efficiency of a grid for better integration of renewable energies. The proposed system can tolerate fluctuating grid demands apart from supporting the features of smart grid, hence viable for increasing stability and sustainability in the grid.
Volume: 14
Issue: 4
Page: 988-998
Publish at: 2025-12-01

Artificial intelligence for individuals with disabilities in higher education institutions: a systematic review

10.11591/ijai.v14.i6.pp4454-4460
Finita Glory Roy , Friggita Johnson
With the growing integration of artificial intelligence (AI) in education, its potential to support students with disabilities in higher education remains significant but underexplored. This systematic review synthesizes existing literature on AI's effectiveness, barriers, and implications for inclusive education. Using the sample, phenomenon of interest, design, evaluation, and research type (SPIDER) framework, studies published between 2013 and 2024 were identified through a systematic search in databases such as PubMed, Scopus, Embase, Cochrane Library, and Google Scholar. Eighteen studies met the inclusion criteria, focusing on higher education settings and students with disabilities. The findings emphasize AI's role in enhancing accessibility, personalizing learning experiences, and fostering inclusiveness. However, persistent challenges include technological barriers, ethical concerns, and insufficient training. While AI holds transformative potential to support students with disabilities in higher education, addressing infrastructure gaps and ethical and training deficiencies is crucial for sustainable implementation and equitable learning environments.
Volume: 14
Issue: 6
Page: 4454-4460
Publish at: 2025-12-01

Enhanced integration of renewable energy and smart grid efficiency with data-driven solar forecasting employing PCA and machine learning

10.11591/ijpeds.v16.i4.pp2645-2654
Jayashree Kathirvel , Pushpa Sreenivasan , M. Vanitha , Soni Mohammed , T. Sathish Kumar , I. Arul Doss Adaikalam
A significant obstacle to preserving grid stability and incorporating renewable energy into smart grids is variations in solar irradiation. To improve solar power management's dependability, this research proposes a short-term solar forecasting framework powered by AI. Multiple machine learning models, such as long short-term memory (LSTM), random forest (RF), gradient boosting (GB), AdaBoost, neural networks (NN), K-Nearest neighbor (KNN), and linear regression (LR), are integrated into the suggested system, which also uses principal component analysis (PCA) for dimensionality reduction. The Abiod Sid Cheikh station in Algeria (2019-2021) provided real-world data for the model's validation. With a two-hour-ahead RMSE of 0.557 kW/m², AdaBoost had the most accuracy, whereas LR had the lowest, at 0.510 kW/m². In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. In addition to increasing computing efficiency, PCA preserved 99.3% of the data volatility. These findings highlight the efficiency of hybrid AI models based on PCA for accurate forecasting, which is crucial for smart grid stability.
Volume: 16
Issue: 4
Page: 2645-2654
Publish at: 2025-12-01

Integrating machine learning and deep learning with landscape metrics for urban heat island prediction

10.11591/ijai.v14.i6.pp4828-4837
Siddharth Pal , Kavita Jhajharia
Elevated temperatures in urban areas relative to surrounding rural areas, known as the urban heat island (UHI) effect, constitute a pressing challenge to urban sustainability, public health, and energy efficiency. With a comprehensive global dataset from NASA's Socioeconomic Data and Applications Center (SEDAC) that encompasses land surface temperature (LST) and different urban characteristics, this study investigates the UHI phenomenon. The UHI intensity was predicted using advanced machine learning models, random forest, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), and long short-term memory (LSTM) with attention mechanism. The LSTM with attention achieved top R2:0.9998 (day) and 0.9992 (night). Key landscape metrics include urban area size, population, and location. We analyzed spatial temporal UHI patterns to identify local factors like geometry and vegetation. These findings are critical for urban planners and policy makers to identify targeted mitigation options, including green space expansion, the use of low thermal mass, and urban climate resilience strategies. These results advance predictive modeling, supporting resilient, and sustainable cities.
Volume: 14
Issue: 6
Page: 4828-4837
Publish at: 2025-12-01

Semantic search-enhanced healthcare chatbot for hospital information management system using vector database and transformer models

10.11591/ijai.v14.i6.pp4600-4613
Erda Guslinar Perdana , Arya Adhi Nugraha
Healthcare chatbots are increasingly used to assist hospital staff, yet most existing systems rely on rule-based or generic machine learning (ML) approaches that lack the ability to comprehend natural language queries, while proprietary deep learning systems often incur high licensing costs. This work addresses this gap by proposing a cost-effective and scalable semantic vector retrieval solution for user intent recognition in a hospital information management system (HIMS) helpdesk chatbot. The MPNet based transformer model is employed to convert user inquiries and predefined intents into feature vectors, enabling highly accurate natural language understanding through cosine similarity retrieval within a dedicated vector database. The proposed vector search method was validated via an ablation study, achieving an accuracy of 0.70 for intent recognition, which demonstrates a significant performance gain of 28.0 percentage points over a traditional keyword-based search baseline. Usability testing across developer and doctor groups yielded an average score of 7.78 on a 10-point Likert scale. This study concludes that integrating semantic vector retrieval with a vector database is highly effective for recognizing specialized clinical intents, offering a more accurate solution that significantly reduces the manual helpdesk workload and enhances 24-hour assistance in healthcare.
Volume: 14
Issue: 6
Page: 4600-4613
Publish at: 2025-12-01
1 2 3
...
1994 1995
Show 89 of 1995

Discover Our Library

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

Explore Now
Library 3D Ilustration