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28,451 Article Results

Image analysis and machine learning techniques for accurate detection of common mango diseases in warm climates

10.11591/ijai.v14.i4.pp2935-2944
Md Abdullah Al Rahib , Naznin Sultana , Nirjhor Saha , Raju Mia , Monisha Sarkar , Abdus Sattar
Mangoes are valuable crops grown in warm climates, but they often suffer from diseases that harm both the trees and the fruits. This paper proposes a new way to use machine learning to detect these diseases early in mango plants. We focused on common issues like mango fruit diseases, leaf diseases, powdery mildew, anthracnose/blossom blight, and dieback, which are particularly problematic in places like Bangladesh. Our method starts by improving the quality of images of mango plants and then extracting important features from these images. We use a technique called k-means clustering to divide the images into meaningful parts for analysis. After extracting ten key features, we tested various ways to classify the diseases. The random forest algorithm stood out, accurately identifying diseases with a 97.44% success rate. This research is crucial for Bangladesh, where mango farming is essential for the economy. By spotting diseases early, we can improve mango production, quality, and the livelihoods of farmers. This automated system offers a practical way to manage mango diseases in regions with similar climates.
Volume: 14
Issue: 4
Page: 2935-2944
Publish at: 2025-08-01

Enhancing precision agriculture: a comprehensive investigation into pathogen detection and management

10.11591/ijai.v14.i4.pp3121-3132
Shaista Farhat , Chokka Anuradha
Agriculture is an important sector of Indian agronomy for human livelihood. All areas are affected by the effects of environmental toxic farms, which makes managing various difficult situations more challenging. Agriculture must adopt new technology in accordance with daily environmental changes if it is going to benefit from a crop from the perspectives of farmers and end users. Farmers will benefit from early detection of agricultural diseases rather than risking their lives in dangerous circumstances. Computer technology will be very helpful in maintaining sustainable and healthy crops for the objective of identifying crop diseases in addition to the farmer's close observation. Deep learning (DL) techniques are very influential among various computing technologies. In this work, we explore several current approaches to precision agriculture, such as artificial intelligence (AI), DL, and machine learning (ML). The findings of the study make clear modern methods, their drawbacks, and the knowledge lacking that needs to be addressed to explore precision agriculture fully.
Volume: 14
Issue: 4
Page: 3121-3132
Publish at: 2025-08-01

Investigation on low-performance tuned-regressor of inhibitory concentration targeting the SARS-CoV-2 polyprotein 1ab

10.11591/ijai.v14.i4.pp3003-3013
Daniel Febrian Sengkey , Angelina Stevany Regina Masengi , Alwin Melkie Sambul , Trina Ekawati Tallei , Sherwin Reinaldo Unsratdianto Sompie
Hyperparameter tuning is a key optimization strategy in machine learning (ML), often used with GridSearchCV to find optimal hyperparameter combinations. This study aimed to predict the half-maximal inhibitory concentration (IC50) of small molecules targeting the SARS-CoV-2 replicase polyprotein 1ab (pp1ab) by optimizing three ML algorithms: histogram gradient boosting regressor (HGBR), light gradient boosting regressor (LGBR), and random forest regressor (RFR). Bioactivity data, including duplicates, were processed using three approaches: untreated, aggregation of quantitative bioactivity, and duplicate removal. Molecular features were encoded using twelve types of molecular fingerprints. To optimize the models, hyperparameter tuning with GridSearchCV was applied across a broad parameter space. The results showed that the performance of the models was inconsistent, despite comprehensive hyperparameter tuning. Further analysis showed that the distribution of Murcko fragments was uneven between the training and testing datasets. Key fragments were underrepresented in the testing phase, leading to a mismatch in model predictions. The study demonstrates that hyperparameter tuning alone may not be sufficient to achieve high predictive performance when the distribution of molecular fragments is unbalanced between training and testing datasets. Ensuring fragment diversity across datasets is crucial for improving model reliability in drug discovery applications.
Volume: 14
Issue: 4
Page: 3003-3013
Publish at: 2025-08-01

Integrating random forest and genetic algorithms for improved kidney disease prediction

10.11591/ijai.v14.i4.pp2797-2804
Bommanahalli Venkatagiriyappa Raghavendr , Anandkumar Ramappa Annigeri , Jogipalya Shivananjappa Srikantamurthy , Gururaj Raghavendrarao Sattigeri
This work offers a novel method for predicting chronic kidney disease (CKD) by combining random forest (RF) classification with genetic algorithm (GA) to optimize important parameters. The dataset comprises 1,659 patients with 51 clinical parameters. The suggested method emphasizes the optimization of random state values, test size, and essential hyperparameters, such as the number of trees in the forest, the least number of samples needed at a leaf node, and the smallest number of samples necessary to split an internal node. The optimization process is conducted in two stages: the first stage optimizes the random state and test size, while the second stage focuses on hyperparameters. Through extensive simulations over 50 runs, the study demonstrates that the optimized model achieves an accuracy ranging from 0.9451 to 0.9738. The results indicate a maximum increase in accuracy of 2.09%, showcasing the effectiveness of the GA-RF integrated approach in enhancing model performance. This work provides valuable insights into the impact of parameter optimization on machine learning (ML) models, particularly in medical diagnostics, and offers a robust framework for developing highly accurate predictive models.
Volume: 14
Issue: 4
Page: 2797-2804
Publish at: 2025-08-01

Lightweight mutual authentication protocol for resource-constrained radio frequency identification tags with PRINCE cipher

10.11591/ijai.v14.i4.pp3435-3443
Mahendra Shridhar Naik , Desai Karanam Sreekantha , Kanduri V S S S S Sairam , Chaitra Soppinahally Nataraju
Radio frequency identification (RFID) is a key technology for the internet of things (IoT), with widespread applications in the commercial, healthcare, enterprise, and community sectors. However, privacy and security concerns remain with RFID systems. This manuscript presents a novel RFID-based mutual authentication protocol (MAP) using the PRINCE cipher to address these concerns. The proposed MAP leverages a PRINCE cipher architecture capable of both encryption and decryption based on a mode signal. It performs five encryption and two decryption processes during tag and reader mutual authentication, with updated seed values ensuring synchronization and secure data communication. The PRINCE cipher implementation utilizes less than 1% of slices, operates at 226 MHz with a latency of 3.5 clock cycles (CC), and has a throughput of 4.125 Gbps. The complete RFID-based MAP consumes 721 mW of power, occupies 2% of the chip area, and achieves a latency of 35.5 CC and a throughput of 262 Mbps. This represents a 25% reduction in latency, a 40% increase in throughput, and a 30% decrease in execution time compared to existing MAP approaches. The findings demonstrate the potential of the proposed MAP to enhance latency, throughput, and execution time, offering a promising solution for secure and efficient RFID authentication.
Volume: 14
Issue: 4
Page: 3435-3443
Publish at: 2025-08-01

Impact of batch size on stability in novel re-identification model

10.11591/ijai.v14.i4.pp2724-2733
Mossaab Idrissi Alami , Abderrahmane Ez-zahout , Fouzia Omary
This research introduces ConvReID-Net, a custom convolutional neural network (CNN) developed for person re-identification (Re-ID) focusing on the batch size dynamics and their effect on training stability. The model architecture consists of three convolutional layers, each followed by batch normalization, dropout, and max-pooling layers for regularization and feature extraction. The final layers include flattened and dense layers, optimizing the extracted features for classification. Evaluated over 50 epochs using early stopping, the network was trained on augmented image data to enhance robustness. The study specifically examines the influence of batch size on model performance, with batch size 64 yielding the best balance between validation accuracy (96.68%) and loss (0.1962). Smaller (batch size 32)and larger (batch size 128) configurations resulted in less stable performance, underscoring the importance of selecting an optimal batch size. These findings demonstrate ConvReID-Net’s potential for real-world Re-ID applications, especially in video surveillance systems. Future work will focus on further hyperparameter tuning and model improvements to enhance training efficiency and stability.
Volume: 14
Issue: 4
Page: 2724-2733
Publish at: 2025-08-01

Development and evaluation of a smart home energy management system using internet of things and real-time monitoring

10.11591/ijece.v15i4.pp3977-3985
Mohamed Imran Mohamed Ariff , Nur Anim Abdul Halim , Mohammad Nasir Abdullah , Samsiah Ahmad , Masurah Mohamad , Anis Zafirah Azmi
This project presents the design and implementation of a smart home energy management system using internet of things (IoT) technology to optimize household energy consumption. The system integrates various sensors, including passive infrared (PIR), light dependent resistor (LDR), and DHT11, to collect real-time environmental data, which is processed by a NodeMCU microcontroller. The microcontroller controls home appliances using relays, while the Blynk mobile app and Streamlit web platform provide users with remote monitoring and control capabilities. Despite successfully optimizing energy usage, the system faces limitations such as high sensor sensitivity and potential hazards during high-load power demonstrations. To address these issues, future work proposes integrating additional sensors for improved accuracy and incorporating renewable energy sources for increased sustainability. This project aims to enhance energy efficiency, provide users with greater control over their energy consumption, and contribute to smart home automation by utilizing real-time data, IoT integration, and user-friendly interfaces.
Volume: 15
Issue: 4
Page: 3977-3985
Publish at: 2025-08-01

Blockchain as a digital governance tool: A systematic review

10.11591/ijece.v15i4.pp3986-3995
Cesar Patricio-Peralta , Jimmy Ramirez Villacorta , Milton Amache Sánchez , Jacker Paredes Meneses , Jesús Zamora Mondragon , Luis Segura Terrones , Paul Torres Santos , César Veliz Manrique , Walter Patricio Peralta
This systematic review explores the implementation of blockchain technology as a digital governance tool, focusing specifically on the Peruvian context. In the digital transformation era, blockchain has established itself as an innovative solution to manage and authenticate information. This research focuses on optimizing administrative and governmental processes in Peru, a country where document verification is crucial in legal, financial, educational, and medical procedures. The methodology used follows the problem/population, intervention, comparison, outcome, context (PICOC) model. 56 high-impact articles were selected in Scopus, prioritizing those in the areas of engineering, computer science, and business, and published between 2022 and 2025. The objective was to define the scope and structure of the research questions. These questions address the implementation of blockchain and its applications in digital governance to ensure security and reliability in administrative procedures. Through a comprehensive literature review, we seek to provide a comprehensive view of how blockchain could transform the interaction between citizens and the Peruvian government by automating document verification. In addition, successful cases from other countries and similar sectors will be analyzed, evaluating their feasibility and applicability in the Peruvian context. This approach will allow us to identify both the potential benefits and the challenges and implications associated with the integration of blockchain into government processes in Perú.
Volume: 15
Issue: 4
Page: 3986-3995
Publish at: 2025-08-01

Navigating cyber investigations: strategies and tools for forensic data acquisition

10.11591/ijece.v15i4.pp4022-4030
Srinivas Kanakala , Vempaty Prashanthi , K. V. Sharada
The rapid proliferation of cybercrimes has underscored the critical importance of robust data acquisition methodologies in the field of digital forensics. This research publication explores various aspects of forensic data acquisition, focusing on techniques, tools, and best practices employed by forensic investigators to collect and preserve digital evidence effectively. Beginning with an overview of the escalating cyber threat landscape and the consequential need for forensic investigations, the publication delves into the fundamental concepts of data acquisition, emphasizing the significance of ensuring data integrity and admissibility in legal proceedings. It examines the process of acquiring both volatile and non-volatile data from diverse sources, including hard drives, RAM, and other digital storage media. Furthermore, evaluates a range of forensic imaging and validation methods, encompassing tools such as Belkasoft live RAM capturer, AccessData FTK Imager, and ProDiscover, alongside validation techniques using PowerShell utility and commercial forensic software. Through comprehensive analysis and discussion, this study serves as a valuable resource for forensic practitioners, researchers, and legal professionals seeking to enhance their understanding of forensic data acquisition methodologies in the ever-evolving landscape of cybercrime investigation.
Volume: 15
Issue: 4
Page: 4022-4030
Publish at: 2025-08-01

Integrating time-frequency features with deep learning for lung sound classification

10.11591/ijece.v15i4.pp3737-3747
Su Yuan Chang , Marni Azira Markom , Zhi Sheng Choong , Arni Munira Markom , Latifah Munirah Kamaruddin , Erdy Sulino Mohd Muslim Tan
Deep learning has transformed medical diagnostics, especially in analyzing lung sounds to assess respiratory conditions. Traditional methods like CT scans and X-rays are impractical in resource-limited settings due to radiation exposure and time consumption, while conventional stethoscopes often lead to misdiagnosis due to subjective interpretation and environmental noise. This study evaluates deep learning models for lung sound classification using the International Conference on Biomedical Health Informatics 2017 dataset, comprising 920 annotated samples from 126 subjects. Pre-processing includes down sampling, segmentation, normalization, and audio clipping, with feature extraction techniques like spectrogram and Mel-frequency cepstral coefficients (MFCC). The adopted automatic lung sound diagnosis network (ASLD-Net) model with triple feature input (time domain, spectrogram, and MFCC) achieved the highest accuracy at 97.25%, followed by the dual feature model (spectrogram and MFCC) at 95.65%. Single-input models with spectrogram and MFCC performed well, while the time domain input alone had the lowest accuracy.
Volume: 15
Issue: 4
Page: 3737-3747
Publish at: 2025-08-01

Insights from the vision-mission statements of Philippine and other ASEAN universities: a K-means clustering analysis

10.11591/ijai.v14.i4.pp3386-3394
Julius Ceazar G. Tolentino , John Paul P. Miranda
This study analyzed the vision and mission statements (VMS) of 117 Philippine state universities and colleges (SUCs) and compared them with 330 other ASEAN universities to identify thematic trends and institutional priorities. Using web scraping and K-means clustering, the study identified thematic clusters in VMS. Thematic trends through word frequency and collocation analyses provided further insights and a comparative analysis examined differences between Philippine SUCs and other ASEAN universities. Philippine SUCs’ vision statements formed three clusters: global competitiveness, premier recognition, and regional leadership in science and technology. Mission statements clustered into: mandated functions, global innovation, and advancement in the sciences. Philippine SUCs emphasized institutional prestige, workforce development, and sustainability while other ASEAN universities focus more on knowledge creation, student empowerment, and internationalization. Philippine SUCs aligned their VMS with national development and global ranking metrics and prioritizes institutional recognition and economic contributions more than the other ASEAN universities. Future studies should expand to more private institutions and international comparisons to assess broader higher education trends.
Volume: 14
Issue: 4
Page: 3386-3394
Publish at: 2025-08-01

A novel fuzzy logic based sliding mode control scheme for non-linear systems

10.11591/ijai.v14.i4.pp2676-2688
Abdul Kareem , Varuna Kumara
Sliding mode control (SMC) has been widely used in the control of non-linear systems due to many inherent properties like superposition, multiple isolated equilibrium points, finite escape time, limit cycle, bifurcation. This research proposes super-twisting controller architecture with a varying sliding surface; the sliding surface being adjusted by a simple single input-single output (SISO) fuzzy logic inference system. The proposed super-twisting controller utilizes a varying sliding surface with an online slope update using a SISO fuzzy logic inference system. This rotates sliding surface in the direction of enhancing the dynamic performance of the system without compromising steady state performance and stability. The performance of the proposed controller is compared to that of the basic super-twisting sliding mode (STSM) controller with a fixed sliding surface through simulations for a benchmark non-linear system control system model with parametric uncertainties and disturbances. The simulation results have confirmed that the proposed approach has the improved dynamic performance in terms of faster response than the typical STSM controller with a fixed sliding surface. This improved dynamic performance is achieved without affecting robustness, system stability and level of accuracy in tracking. The proposed control approach is straightforward to implement since the sliding surface slope is regulated by a SISO fuzzy logic inference system. The MATLAB/Simulink is used to display the efficiency of proposed system over conventional system.
Volume: 14
Issue: 4
Page: 2676-2688
Publish at: 2025-08-01

English learning perceptions and career implications: insights from tertiary-level students

10.11591/ijere.v14i4.32836
Samia A. Abu El-Haj , Hala Abu El-Haj , Nadia Mustafa Al-Assaf
This study investigates students’ perceptions of English language learning at the tertiary level, focusing on its importance for academic success and future career opportunities. The problem addressed is the gap in understanding how students perceive the relevance of English proficiency to their professional futures and the challenges they face in achieving proficiency. A descriptive-analytical and correlational research design was employed, using data from 127 students across various majors and years of study at the University of Jordan, with 122 valid responses. The research utilized a structured questionnaire to explore students’ learning goals, perceived importance of English proficiency, and the challenges they face in language classes. Statistical analysis was applied to identify significant relationships between variables. The major findings reveal that students view English proficiency, particularly in speaking, as critical for their professional futures. However, challenges such as inadequate classroom resources and limited opportunities for practice were commonly reported. The proposed solution emphasizes the need for enhanced language instruction that aligns with students’ professional goals, alongside improvements in classroom infrastructure and more practical language engagement opportunities. The study concludes that addressing these challenges could significantly improve students’ English learning outcomes and better prepare them for their future careers.
Volume: 14
Issue: 4
Page: 3325-3335
Publish at: 2025-08-01

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

Optimization of 3D rendering algorithms for carbon reduction in virtual reality technology

10.11591/ijeecs.v39.i1.pp399-409
Fendi Aji Purnomo , Fatchul Arifin , Herman Dwi Surjono
Virtual reality (VR) systems are widely used across various domains, yet their high computational demands significantly contribute to energy consumption and carbon emissions. Optimizing rendering algorithms is essential to address these environmental challenges, particularly in multiuser VR environments where efficiency is critical. This study aims to evaluate the effectiveness of various rendering techniques in reducing energy consumption and carbon emissions as optimal solutions for multiuser VR applications. The research methodology followed the PRISMA framework, with a literature search conducted using the Scopus database and keywords such as “virtual reality” and “energy efficiency.” The search yielded 1,374 articles published after 2019, which were screened and narrowed down to 24 critical articles. Results demonstrate that Occlusion Culling achieves up to 85% energy savings per frame, translating to a carbon emission reduction of 76.5 g CO₂/hour, while LOD provides a 50% energy efficiency improvement, reducing carbon emissions by 45 g CO₂/hour. These findings highlight the critical role of these techniques in enhancing the sustainability of VR systems, particularly in multi-user environments, and underscore their potential as key strategies in reducing the environmental footprint of VR technology.
Volume: 39
Issue: 1
Page: 399-409
Publish at: 2025-07-01
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