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31,456 Article Results

Exploring bibliometric trends in speech emotion recognition (2020-2024)

10.11591/ijai.v14.i4.pp3421-3434
Yesy Diah Rosita , Muhammad Raafi'u Firmansyah , Annisaa Utami
Speech Emotion Recognition (SER) is crucial in various real-world applications, including healthcare, human-computer interaction, and affective computing. By enabling systems to detect and respond to human emotions through vocal cues, SER enhances user experience, supports mental health monitoring, and improves adaptive technologies. This research presents a bibliometric analysis of SER based on 68 articles from 2020 to early 2024. The findings show a significant increase in publications each year, reflecting the growing interest in SER research. The analysis highlights various approaches in preprocessing, data sources, feature extraction, and emotion classification. India and China emerged as the most active contributors, with external funding, particularly from the NSFC, playing a significant role in the advancement of SER research. SVM remains the most widely used classification model, followed by KNN and CNN. However, several critical challenges persist, including inconsistent data quality, cross-linguistic variability, limited emotional diversity in datasets, and the complexity of real-time implementation. These limitations hinder the generalizability and scalability of SER systems in practical environments. Addressing these gaps is essential to enhance SER performance, especially for multimodal and multilingual applications. This study provides a detailed understanding of SER research trends, offering valuable insights for future advances in speech-based emotion recognition.
Volume: 14
Issue: 4
Page: 3421-3434
Publish at: 2025-08-01

Exploring user feedback on sharia FinTech apps: a Netnographic study in Indonesia

10.11591/ijict.v14i2.pp663-672
Azhar Alam , Fadhiil Arkanur Raihan , Muhamad Al Bagir , Adityo Wiwit Kurniawan , Jibrail Bin Yusuf
The rapid growth of Sharia FinTech applications in Indonesia has raised questions about user perceptions and experiences. This study employs a Netnographic approach to explore user feedback on Sharia FinTech apps through reviews posted on the Google Play store. The research analyzed 129 reviews from five Sharia FinTech applications between July and December 2023. The study reveals that 55.10% of users expressed overall satisfaction with the apps, appreciating their ease of use and Sharia compliance. However, significant challenges were identified, with 37.50% of negative reviews related to payment delays and interest issues. Other concerns included system errors, account creation difficulties, and poor customer service. These findings highlight the complex dynamics of user experiences with Sharia FinTech applications, demonstrating a generally positive reception but also pointing to critical areas for improvement. The study contributes to the understanding of Sharia FinTech adoption in Indonesia and provides valuable insights for application developers and Islamic microfinance institutions to enhance their services and address user concerns.
Volume: 14
Issue: 2
Page: 663-672
Publish at: 2025-08-01

Consumer behavior switching from human agents to chatbots in the health service industry

10.11591/ijict.v14i2.pp355-365
Dwi Fajar Yulianto , Titik Pratiwi , Fatih Akbarul Irsan , Faranita Mustikasari
Artificial intelligence (AI) technology is used in organizations to replace human services with technology, altering customer service experiences. Only a limited number of studies have explored how consumers change their behavior from human-assisted to technology-assisted services when using AI in frontline and specialty healthcare services. This study examined the elements that impact consumers’ transition from human agents to AI-based conversational agents using the push-pull mooring framework. Data from 147 healthcare users was evaluated using structural equation modeling. The data indicates that push effects, specifically adaptability, have a negative impact on switching behavior, while pull effects, such as responsiveness and accessibility, have a positive impact on the switching behavior of customers.
Volume: 14
Issue: 2
Page: 355-365
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

Traffic flow prediction using long short-term memory-Komodo Mlipir algorithm: metaheuristic optimization to multi-target vehicle detection

10.11591/ijai.v14.i4.pp3343-3353
Imam Ahmad Ashari , Wahyul Amien Syafei , Adi Wibowo
Multi-target vehicle detection in urban traffic faces challenges such as poor lighting, small object sizes, and diverse vehicle types, impacting traffic flow prediction accuracy. This study introduces an optimized long short-term memory (LSTM) model using the Komodo Mlipir algorithm (KMA) to enhance prediction accuracy. Traffic video data are processed with YOLO for vehicle classification and object counting. The LSTM model, trained to capture traffic patterns, employs parameters optimized by KMA, including learning rate, neuron count, and epochs. KMA integrates mutation and crossover strategies to enable adaptive selection in global and local searches. The model's performance was evaluated on an urban traffic dataset with uniform configurations for population size and key LSTM parameters, ensuring consistent evaluation. Results showed LSTM-KMA achieved a root mean square error (RMSE) of 14.5319, outperforming LSTM (16.6827), LSTM-improved dung beetle optimization (IDBO) (15.0946), and LSTM-particle swarm optimization (PSO) (15.0368). Its mean absolute error (MAE), at 8.7041, also surpassed LSTM (9.9903), LSTM-IDBO (9.0328), and LSTM-PSO (9.0015). LSTM-KMA effectively tackles multi-target detection challenges, improving prediction accuracy and transportation system efficiency. This reliable solution supports real-time urban traffic management, addressing the demands of dynamic urban environments.
Volume: 14
Issue: 4
Page: 3343-3353
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

A deep learning-based framework for automatic detection of COVID-19 using chest X-ray and CT-scan images

10.11591/ijai.v14.i4.pp3192-3200
Sivanagireddy Kalli , Bukka Narendra Kumar , Saggurthi Jagadeesh , Kushagari Chandramouli Ravi Kumar
COVID-19 has profoundly impacted global public health, underscoring the need for rapid detection methods. Radiography and radiologic imaging, especially chest X-rays, enable swift diagnosis of infected individuals. This study delves into leveraging machine learning to identify COVID-19 from X-ray images. By gathering a dataset of 9,000 chest X-rays and CT scans from public resources, meticulously vetted by board-licensed radiologists to confirm COVID-19 presence, the research sets a robust foundation. However, further validation is essential expanding datasets to encompass enough COVID-19 cases enhances convolutional neural network (CNN) accuracy. Among various machine learning techniques, deep learning excels in identifying distinct patterns on imaging characteristics discernible in chest radiographs of COVID-19 patients. Yet, extensive validation across diverse datasets and clinical trials is crucial to ensure the robustness and generalizability of these models. The conversation extends into complexities, including ethical considerations around patient privacy and integrating intelligent tech into clinical workflows. Collaborating closely with healthcare professionals ensures this technology complements the established diagnostic approach. Despite the potential to detect COVID-19 using chest X-ray imaging findings, thorough research and validation, alongside ethical deliberations, are vital before implementing it in the healthcare field. The results show that the proposed model achieved classification accuracy and F1 score of 96% and 98%, respectively, for the X-ray images.
Volume: 14
Issue: 4
Page: 3192-3200
Publish at: 2025-08-01

Modified zero-reference deep curve estimation for contrast quality enhancement in face recognition

10.11591/ijai.v14.i4.pp3274-3286
Muhammad Kahfi Aulia , Dyah Aruming Tyas
Face recognition systems remain challenged by variable lighting conditions. While zero-reference deep curve estimation (Zero-DCE) effectively enhances low-light images, it frequently induces overexposure in normal- and high-brightness scenarios. This study introduces modified Zero-DCE combined with three established enhancement techniques: contrast stretching (CS), contrast limited adaptive histogram equalization (CLAHE), and brightness preserving dynamic histogram equalization (BPDHE). Evaluations employed the extended Yale face database B and face recognition technology (FERET) datasets, with 10 representative samples assessed using the blind/referenceless image spatial quality evaluator (BRISQUE) metric. Modified Zero-DCE with BPDHE produced optimal enhancement quality, achieving a mean BRISQUE score of 16.018. On the extended Yale face database B, visual geometry group 16 (VGG16) integrated with modified Zero-DCE and CLAHE attained 83.65% recognition accuracy, representing a 6.08-percentage-point improvement over conventional Zero-DCE. For the 200-subject FERET subset, residual network 50 (ResNet50) with modified Zero-DCE and CLAHE achieved 67.41% accuracy. Notably, standard Zero-DCE with CLAHE demonstrated superior robustness in extremely low-light conditions, highlighting the illumination-dependent performance characteristics of these enhancement approaches.
Volume: 14
Issue: 4
Page: 3274-3286
Publish at: 2025-08-01

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

Optimized pap-smear image enhancement: hybrid Perona-Malik diffusion filter-CLAHE using spider monkey optimization

10.11591/ijai.v14.i4.pp2765-2775
Ach Khozaimi , Isnani Darti , Wuryansari Muharini Kusumawinahyu , Syaiful Anam
Pap-smear image quality is crucial for cervical cancer detection. This study introduces an optimized hybrid approach that combines the Perona-Malik diffusion (PMD) filter with contrast-limited adaptive histogram equalization (CLAHE) to enhance pap-smear image quality. The PMD filter reduces the image noise, whereas CLAHE improves the image contrast. The hybrid method was optimized using spider monkey optimization (SMO PMD-CLAHE). Blind/reference-less image spatial quality evaluator (BRISQUE) and contrast enhancement-based image quality (CEIQ) are the new objective functions for the PMD filter and CLAHE optimization, respectively. The simulations were conducted using the SIPaKMeD dataset. The results indicate that SMO outperforms state-of-the-art methods in optimizing the PMD filter and CLAHE. The proposed method achieved an average effective measure of enhancement (EME) of 5.45, root mean square (RMS) contrast of 60.45, Michelson’s contrast (MC) of 0.995, and entropy of 6.80. This approach offers a new perspective for improving pap-smear image quality.
Volume: 14
Issue: 4
Page: 2765-2775
Publish at: 2025-08-01

Transforming images into words: optical character recognition solutions for image text extraction

10.11591/ijai.v14.i4.pp3412-3420
Jyoti Wadmare , Sunita Patil , Dakshita Kolte , Kapil Bhatia , Palak Desai , Ganesh Wadmare
Optical character recognition (OCR) tool is a boon and greatest advancement in today’s emerging technology which has proven its remarkability in recent years by making it easier for humans to convert the textual information in images or physical documents into text data making it useful for analysis, automation processes and improvised productivity for different purposes. This paper presents the designing, development and implementation of a novel OCR tool aiming at text extraction and recognition tasks. The tool incorporates advanced techniques such as computer vision and natural language processing (NLP) which offer powerful performance for various document types. The performance of the tool is subject to metrics like analysis, accuracy, speed, and document format compatibility. The developed OCR tool provides an accuracy of 98.8% upon execution providing a character error rate of 2.4% and word error rate (WER) of 2.8%. OCR tool finds its applications in document digitization, personal identification, archival of valuable documents, processing of invoices, and other documents. OCR tool holds an immense amount of value for researchers, practitioners and many organizations which seek effective techniques for relevant and accurate text extraction and recognition tasks.
Volume: 14
Issue: 4
Page: 3412-3420
Publish at: 2025-08-01

Cyber-physical resilience system for anomaly detection in industrial environments

10.11591/ijict.v14i2.pp497-505
Debani Prasad Mishra , Rakesh Kumar Lenka , Rampa Sri Sai Yagyna Duthsharma , Pavan Kumar , Lakshay Bhardwaj , Surender Reddy Salkuti
This work explores the topic of cybersecurity in the context of electric vehicles (EVs). It ensures the resilience of cyber-physical systems against anomalies, which is paramount for maintaining operational efficiency and safety. This paper presents a cyber-physical resilience system (CPRS) customized for anomaly detection. Maintaining operational efficiency and safety in today’s networked industrial contexts requires that cyber-physical systems be resilient to abnormalities. With an emphasis on EVs, this research introduces a unique CPRS designed for anomaly detection in industrial settings. By utilizing the combination of digital and physical elements, the CPRS uses sophisticated monitoring and reaction systems to identify and address irregularities instantly. The process includes creating algorithms for anomaly detection and putting in place a framework that is responsive enough to change with the dangers that it faces. The efficiency of the CPRS in detecting unusual behaviors in EVs is demonstrated by experimental findings, which also improve the overall resilience of the system. Moreover, the research’s ramifications go beyond EVs to include a variety of industrial settings, providing valuable information for the development and execution of resilient cyber-physical systems. This paper highlights the significance of proactive resilience measures in protecting critical infrastructure and advances anomaly detection approaches.
Volume: 14
Issue: 2
Page: 497-505
Publish at: 2025-08-01

Revolutionizing internet of things intrusion detection using machine learning with unidirectional, bidirectional, and packet features

10.11591/ijai.v14.i4.pp3047-3062
Zulhipni Reno Saputra Elsi , Deris Stiawan , Bhakti Yudho Suprapto , M. Agus Syamsul Arifin , Mohd. Yazid Idris , Rahmat Budiarto
Detection of attacks on internet of things (IoT) networks is an important challenge that requires effective and efficient solutions. This study proposes the use of various machine learning (ML) techniques in classifying attacks using unidirectional, bidirectional, and packet features. The proposed methods that implement decision tree (DT), random forest (RF), extreme gradient boosting classifier (XGBC), AdaBoost (AB) and linear discriminant analysis (LDA) work perfectly with all kinds of datasets and includes. It also works very well with data type-based feature selection (DTBFS) and correlation-based feature selection (CBFS). The experiment results show a significant improvement compared to previous studies and reveals that unidirectional and bidirectional features provide higher accuracy compared to packet features. Furthermore, ML models, particularly DT, and RF, have faster computing times compared to more complex deep learning models. This analysis also shows potential overfitting in some models, which requires further validation with different datasets. Based on these findings, we recommend the use of RF and DT for scenarios with unidirectional and bidirectional features, while AB and LDA for packet features. The study concludes that using the right ML techniques along with features that work in both directions can make an intrusion detection system for IoT networks becomes very accurate.
Volume: 14
Issue: 4
Page: 3047-3062
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

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
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