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A hybrid framework for enhanced intrusion detection in cloud environments leveraging autoencoder

10.11591/ijict.v14i2.pp555-564
Abinaya Alagarsamy , Thenmozhi Elumalai , S. P. Ramesh , Tamilarasi Karuppiah , Prabu Kaliyaperumal , Rajakumar Perumal
In today’s world, the significance of network security and cloud environments has grown. The rising demand for data transmission, along with the versatility of cloud-based solutions and widespread availability of global resources, are key drivers of this growth. In response to rapidly evolving threats and malicious attacks, developing a robust intrusion detection system (IDS) is essential. This study addresses the imbalanced data and utilizes an unsupervised learning approach to protect network data. The suggested hybrid framework employs the CIC-IDS2017 dataset, integrating methods for handling imbalanced data with unsupervised learning to enhance security. Following preprocessing, principal component analysis (PCA) reduces the dimensionality from eighty features to twenty-three features. The extracted features are input into density-based spatial clustering of applications with noise (DBSCAN), a clustering algorithm. particle swarm optimization (PSO) optimizes DBSCAN, grouping similar traffic and enhancing classification. To address the imbalances in the learning process, the autoencoder (AE) algorithm demonstrates unsupervised learning. The data from the cluster is input into the AE, a deep learning algorithm, which classifies traffic as normal or an attack. The proposed approach (PCA+DBSCAN+AE) attains remarkable intrusion detection accuracy exceeding 98%, and outperforms five contemporary methodologies.
Volume: 14
Issue: 2
Page: 555-564
Publish at: 2025-08-01

Techniques of deep learning neural network-based building feature extraction from remote sensing images: a survey

10.11591/ijict.v14i2.pp614-624
Shrinivas B. Khandare , Manoj B. Chandak
Recently, due to earthquake disaster, many people have lost their lives and homes, and not able to settle to new locations immediately. Therefore, a framework or a plan should be ready to immediately relocate the people to different locations or do resettlement. Much research has been done in this field but still there are problems of identifying clear building boundaries, rectangular houses, due to the problem of different shapes of the buildings. These techniques were explored for identification of clear building boundaries, rectangular houses, buildings which are more highlighted and smaller size buildings for pre-disaster and post-disaster building extraction scenarios. In this survey of building extraction techniques, most of the approach is training the network, second approach is refining the trained output features, running the trained samples on the predefined models of neural network. Several issues and their assessment are studied in these techniques. These are beneficial to the various researchers for different building extractions.
Volume: 14
Issue: 2
Page: 614-624
Publish at: 2025-08-01

Enhanced n-party Diffie Hellman key exchange algorithm using the divide and conquer algorithm

10.11591/ijict.v14i2.pp438-445
Nwanze Chukwudi Ashioba , Patrick Ogholorunwalomi Ejeh , Azaka Maduabuchuku
Cryptographic algorithms guarantee data and information security via a communication system against unauthorized users or intruders. Numerous encryption techniques have been employed to safeguard this data and information from hackers. By supplying a distinct shared secret key, the n-party Diffie Hellman key exchange approach has been used to protect data from hackers. Using a quadratic time complexity, the n-party Diffie-Hellman method is slow when multiple users use the cryptographic key interchange system. To solve this issue, the researchers created an effective shared hidden key for the n-party Diffie Hellman key exchange of a cryptographic system using the divide-and-conquer strategy. The current research recommends the use of the divide and conquer algorithm, which breaks down the main problem into smaller subproblems until it reaches the base solution, which is then merged to generate the solution of the main problem. The comparative analysis indicates that the developed system generates a shared secret key faster than the current n-party Diffie Hellman system.
Volume: 14
Issue: 2
Page: 438-445
Publish at: 2025-08-01

Cloud application design for financial reporting in Indonesia’s small and medium enterprises

10.11591/ijict.v14i2.pp457-466
Erin Erin , Anderes Gui
Small and medium enterprises (SMEs) in Indonesia are increasingly developing, but the application of information technology (IT) in small medium businesses is still lacking because for small medium business owners, doing their own bookkeeping without a system will maximize profits. However, this makes bookkeeping ineffective and inefficient because it requires manual data input and reconciliation. Utilizing a cloud-based accounting information system (CAIS) can integrate data, increase productivity, and minimize infrastructure costs because there is no need to provide costs for physical infrastructure. In this research, CAIS was designed to produce financial reports that focus on small medium businesses in Indonesia. The method used is a qualitative method by conducting observations through literature study for data collection and the rational unified process (RUP) which is limited to the elaboration stage to produce a prototype design. So, the result of this paper is a system design that can be used as a guide to continue with system development. This system aims to simplify transaction records so that they can be more efficient and effective in producing financial reports. The use of CAIS is also expected to increase profits and maximize the use of internet and technology in small medium businesses.
Volume: 14
Issue: 2
Page: 457-466
Publish at: 2025-08-01

Pioneering the digital readiness for Malaysian museums: custom framework

10.11591/ijict.v14i2.pp728-736
Rehman Ullah Khan , Sabine Chung Sze Yee , Oon Yin Bee
A museum is a hub for public exploration and education of community or country culture and traditions. Digital technologies transform museums into interactive experiences, engaging visitors and bringing cultural values to life. However, Malaysian museums struggle to adopt digital technologies due to limited infrastructure, expertise, exhibition technology, and budgets. These constraints hinder effective audience engagement and limit growth and modernisation efforts. To help Malaysian museums in digitalisation, this study aims to contextualise a digital readiness index (DRI) questionnaire. The findings of this pioneering study have yielded a unique and customised version of the DRI questionnaire specifically designed for Malaysian museums, marking the first-ever initiative of its kind in the country. The DRI serves as a pivotal scale or tool for managers and researchers, facilitating the evaluation and validation of a museum’s digitalisation status while guiding strategic planning for future advancements. This questionnaire enables researchers and museum managers to gain insights into the museums and understand which dimensions require focus and enhancement to ensure a successful and comprehensive transition towards digital transformation.
Volume: 14
Issue: 2
Page: 728-736
Publish at: 2025-08-01

Automated rice leaf disease detection using artificial intelligence deep learning

10.11591/ijict.v14i2.pp405-415
Suhaila M. P. , Hemalatha S.
As one of the top five rice-producing countries, India relies heavily on rice for both economic management and food needs. To ensure healthy rice plant growth, early detection of diseases and timely treatment are essential. Since manual disease detection is time-consuming and labor-intensive, an automated approach is more practical. This work presents a deep neural network (DNN)-based artificial intelligence (AI) method for recognizing rice leaf diseases. The method detects three common diseases: leaf smut, bacterial leaf blight, and brown spot, as well as healthy images. The approach uses an AI-based attention network and semantic batch normalized DeepNet (AN-SBNDN) combined with a channel attention mechanism to improve disease detection accuracy. Experiments with rice leaf datasets and comparison with conventional networks like residual attention network (Res ATTEN) and dynamic speeded up robust features (DSURF) validate the effectiveness of the method. Key performance metrics include average accuracy, time, precision, and recall, achieved at 21%, 44%, 26%, and 31%, respectively.
Volume: 14
Issue: 2
Page: 405-415
Publish at: 2025-08-01

Prediction and classification of diabetic retinopathy using machine learning techniques

10.11591/ijict.v14i2.pp516-528
Makhlouf Chaouki , Mohamed Ridda Laouar , Abbas Cheddad , Bourougaa Salima , Sean Eom
Diabetic retinopathy (DR) is a progressive and sight-threatening complication of diabetes mellitus, characterized by damage to the blood vessels in the retina. Early detection of DR is vital for timely intervention and effective management to prevent irreversible vision loss. This paper provides a comprehensive review of recent advancements in integrating machine learning (ML) and deep learning (DL) techniques for diagnosing DR, aiming to assist ophthalmologists in their manual diagnostic process. The paper presents a comprehensive definition of DR, elucidating the underlying pathological processes, clinical signs, and the various stages of DR classification, ranging from mild non-proliferative to severe proliferative DR. Integrating ML and DL in DR diagnosis has developed the field by offering automated and efficient methods and techniques to analyze retinal images. With high sensitivity and specificity, these techniques demonstrate their efficacy in accurately identifying DR-related lesions, such as microaneurysms, exudates, and hemorrhages. Furthermore, the paper examines diverse datasets employed in training and evaluating ML and DL models for DR diagnosis. These datasets range from publicly available repositories to specialized datasets curated by medical institutions. The role of large-scale and diverse datasets in enhancing model robustness and generalizability is emphasized.
Volume: 14
Issue: 2
Page: 516-528
Publish at: 2025-08-01

Malware detection using Gini, Simpson diversity, and Shannon-Wiener indexes

10.11591/ijict.v14i2.pp737-750
Yeong Tyng Ling , Kang Leng Chiew , Piau Phang , Xiaowei Zhang
The increasing number of malware attacks poses a significant challenge to cyber security. This paper proposes a methodology for static malware analysis using biodiveristy-inspired metrics that is Gini coefficient, Simpson diversity, and Shannon-Wiener index for malware detection. These metrics are used to build the structural feature representation on the raw binary file as the feature space. The effectiveness of these metrics are evaluated using multilayer perceptron (MLP) neural network and extreme gradient boosting (XGBoost) models. A deterministic algorithm is used to generate these features that represent the feature signature of the executable file. Additionally, we investigated the effectiveness of different byte sizes as the input feature for these two classifiers. According to the results, Gini coefficient with on chunk size of 128 has successfully achieved average F1 score of more than 98.7% by using XGBoost model.
Volume: 14
Issue: 2
Page: 737-750
Publish at: 2025-08-01

Myoelectric grip force prediction using deep learning for hand robot

10.11591/ijai.v14.i4.pp3228-3240
Khairul Anam , Dheny Dwi Ardhiansyah , Muchamad Arif Hana Sasono , Arizal Mujibtamala Nanda Imron , Naufal Ainur Rizal , Mochamad Edoward Ramadhan , Aris Zainul Muttaqin , Claudio Castellini , Sumardi Sumardi
Artificial intelligence (AI) has been widely applied in the medical world. One such application is a hand-driven robot based on user intention prediction. The purpose of this research is to control the grip strength of a robot based on the user’s intention by predicting the grip strength of the user using deep learning and electromyographic signals. The grip strength of the target hand is obtained from a handgrip dynamometer paired with electromyographic signals as training data. We evaluated a convolutional neural network (CNN) with two different architectures. The input to CNN was the root mean square (RMS) and mean absolute value (MAV). The grip strength of the hand dynamometer was used as a reference value for a low-level controller for the robotic hand. The experimental results show that CNN succeeded in predicting hand grip strength and controlling grip strength with a root mean square error (RMSE) of 2.35 N using the RMS feature. A comparison with a state-of-the-art regression method also shows that a CNN can better predict the grip strength.
Volume: 14
Issue: 4
Page: 3228-3240
Publish at: 2025-08-01

The design of an electronic load for mitigating transient overvoltage in the track circuits of railway signaling systems

10.11591/ijeecs.v39.i2.pp807-820
Ukrit Kornkanok , Sansak Deeon , Chuthong Summatta , Saktanong Wongcharoen
The research presented the design of safety electronic load suppression (SELS) for mitigating transient overvoltage in the track circuits of railway signaling systems while changing the track occupancy in the track circuits of the signaling system that caused damage to the BR966F2 relay. The analysis of the average failure of the electronic devices, the failure modes and effect analysis (FMEA), and the performance test of electronic devices were conducted. and the performance test of electronic devices were conducted. which can control the operation with 2oo3 processing mode (two out of three voting) under the series circuits pattern to resolve the damage caused by the application. Results illustrated that the mean operating time of the SELS between failures was 9,399 hours. In addition, regarding the performance of the electronic load for mitigating transient overvoltage of 1 kV at 31.4 V and overvoltage 50 VDC at 178.6 °C within 83 seconds at 35.4 V. Additionally, the SELS could function adequately without failure or causing any damage. Therefore, the SELS was more reliable.
Volume: 39
Issue: 2
Page: 807-820
Publish at: 2025-08-01

An innovative approach for predictive modeling and staging of chronic kidney disease

10.11591/ijict.v14i2.pp684-707
Safa Boughougal , Mohamed Ridda Laouar , Abderrahim Siam , Sean Eom
Diagnosing silent diseases such as chronic kidney disease (CKD) at an early stage is challenging due to the absence of symptoms, making early detection crucial to slowing disease progression. This study addresses this challenge by introducing a novel feature, the estimated glomerular filtration rate (eGFR), calculated using the modification of diet in renal disease (MDRD) formula. We enriched our dataset by incorporating this feature, effectively increasing the volume of data at our disposal. eGFR serves as a critical indicator for diagnosing CKD and assessing its progression, thereby guiding clinical management. Our focus is on developing machine learning and deep learning models for the efficient and precise prediction of CKD. To ensure the reliability of our approach, we employed robust data collection and preprocessing techniques, resulting in refined information for model training. Our methodology integrates various machine learning and deep learning models, including four machine learning algorithms: adaptive boosting (AdaBoost), random forest (RF), Bagging, and artificial neural network (ANN), as well as a hybrid model. Our proposed ANN_AdaBoost model not only introduces a novel perspective by addressing an identified gap but significantly enhances CKD prediction.
Volume: 14
Issue: 2
Page: 684-707
Publish at: 2025-08-01

Interoperability in healthcare: a critical review of ontology approaches and tools for building prescription frameworks

10.11591/ijict.v14i2.pp366-381
Eunice Chinatu Okon , Tshiamo Sigwele , Malatsi Galani , Tshepiso Mokgetse , Hlomani Hlomani
Efficient healthcare interoperability is pivotal for delivering high-quality patient care. This research article presents a critical review of ontology based approaches and tools in the development of ontology-based electronic prescriptions (e-prescription), with a focus on enhancing healthcare interoperability. The investigation encompasses two major domains: ontology overview and healthcare interoperability using semantic e-prescription. In the ontology overview, we scrutinize various aspects of ontology development, including the methodologies, languages, tools, and evaluation metrics adopted from literature. Notable comparisons between ontologies and databases are explored. Additionally, we delve into the challenges associated with ontology development and provide a comprehensive summary of methodologies, languages, tools, and evaluation approaches. Healthcare interoperability using semantic e-prescription undertakes a detailed review of e-prescription systems, emphasizing their critical role in healthcare interoperability. A thorough examination of frameworks facilitating semantic e-prescription is presented, offering a nuanced perspective on their contributions and limitations. The section concludes with a concise summary of the key findings from the e-prescription framework review. The article further addresses challenges in healthcare interoperability, including data standardization and system integration issues. To direct continuing research efforts that integrate cutting-edge technologies and interdisciplinary collaborations, future directions and emerging trends are outlined.
Volume: 14
Issue: 2
Page: 366-381
Publish at: 2025-08-01

An IoT-based approach for microclimate surveillance in greenhouse environments

10.11591/ijict.v14i2.pp717-727
Irfan Ardiansah , Sophia Dwiratna Nur Perwitasari , Roni Kastaman , Totok Pujianto
As the demand for efficient and cost-effective greenhouse microclimate surveillance has increased, we developed a microclimate surveillance system using microcontroller technology that automatically collects temperature and relative humidity data and transmits it to a cloud server for remote surveillance and data analysis. 1971 microclimate data points were acquired over a 20-day evaluation period, providing insights into greenhouse environmental conditions with a negative linear regression between air temperature and relative humidity within the greenhouse and an R-squared of 0.73. This illustrates the system’s ability to record and quantify environmental conditions data. Additionally, we derived a predictive model to manage microclimate conditions using the regression formula y = -6.12X + 238.33, where X represents the air temperature and y represents the relative humidity. All the results show that the acquired data can be used to make decisions to optimize plant growth. The prototype we developed can be an alternative option for small and medium-sized farms that need a greenhouse surveillance system to improve operational efficiency and reduce surveillance costs. The system can be further developed by implementing additional sensors to monitor light intensity, wind speed, or soil moisture and further data analysis models to optimize greenhouse management.
Volume: 14
Issue: 2
Page: 717-727
Publish at: 2025-08-01

Machine learning for global trade analysis: a hybrid clustering approach using DBSCAN, elbow, and SOM

10.11591/ijai.v14.i4.pp3033-3046
Musdalifa Thamrin , Ida Mulyadi , I Dewa Made Widia , Muhammad Faisal , Suardi Hi Baharuddin , Medy Wismu Prihatmono , Nurdiansyah Nurdiansyah , Nasir Usman
Global trade constitutes a highly complex and interdependent system influenced by diverse economic, geographic, and political factors. This study proposes a hybrid clustering framework that integrates density-based spatial clustering of applications with noise (DBSCAN), elbow, and self-organizing maps (SOM) methods to uncover latent structures in international trade patterns. Utilizing averaged trade data from 25 countries spanning the period from 2013 to 2023, the framework identifies distinct clusters based on export-import characteristics. The DBSCAN is employed to detect dense trade hubs and outlier behaviors, the elbow method determines the optimal number of clusters, and SOM facilitates the visualization of non-linear, high-dimensional trade relationships. The analysis reveals three prominent trade clusters: Global Trade Leaders, Emerging Trade Powers, and Niche Exporters, each reflecting varying degrees of trade diversification and dependency. These empirical findings align with established economic theories, including the Heckscher Ohlin model and dependency theory, and provide actionable insights for policymakers seeking to enhance trade competitiveness and regional integration strategies.
Volume: 14
Issue: 4
Page: 3033-3046
Publish at: 2025-08-01

Optimizing long short-term memory hyperparameter for cryptocurrency sentiment analysis with swarm intelligence algorithms

10.11591/ijai.v14.i4.pp2753-2764
Kristian Ekachandra , Dinar Ajeng Kristiyanti
This study investigates the application of swarm intelligence algorithms, specifically particle swarm optimization (PSO), ant colony optimization (ACO), and cat swarm optimization (CSO), to optimize long short-term memory (LSTM) networks for sentiment analysis in the context of cryptocurrency. By leveraging these optimization techniques, we aimed to enhance both the accuracy and computational efficiency of LSTM models by fine-tuning critical hyperparameters, notably the number of LSTM units. The study involved a comparative analysis of LSTM models optimized with each algorithm, evaluating performance metrics such as accuracy, loss, and execution time. Results indicate that the PSO-LSTM model achieved the highest accuracy at 86.08% and the lowest loss at 0.57, with a reduced execution time of 58.43 seconds, outperforming both ACO-LSTM and CSO-LSTM configurations. These findings underscore the effectiveness of PSO in tuning LSTM parameters and emphasize the potential of swarm intelligence for enhancing neural network performance in real-time sentiment analysis applications. This research contributes to advancing optimized deep learning techniques in high dimensional data environments, with implications for improving cryptocurrency sentiment predictions.
Volume: 14
Issue: 4
Page: 2753-2764
Publish at: 2025-08-01
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