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29,939 Article Results

An model for structured the NoSQL databases based on machine learning classifiers

10.11591/ijict.v14i1.pp229-239
Amine Benmakhlouf
Today, the majority of data generated and processed in organizations is unstructured. NoSQL database management systems perform the management of this data. The problem is that these unstructured databases cannot be analyzed by traditional OLAP analytical treatments. The latter are mainly used in structured relational databases. In order to apply OLAP analyses on NoSQL data, the structuring of this data is essential. In this paper, we propose a model for structuring the data of a document-oriented NoSQL database using machine learning (ML). This method is broken down into three steps, first the vectorization of documents, then the learning via different ML algorithms and finally the classification, which guarantees that documents with the same structure will belong to the same collection. Therefore, the modeling of a data warehouse can be carried out in order to create OLAP cubes. Since the models found by learning allow the parallel computation of the classifier, our approach represents an advantage in terms of speed since we will avoid doubly iterative algorithms, which rely on textual comparisons (TC). A comparative study of the performances is carried out in this work in order to detect the most efficient methods to perform this type of classification.
Volume: 14
Issue: 1
Page: 229-239
Publish at: 2025-04-01

Enhancing predictive modelling and interpretability in heart failure prediction: a SHAP-based analysis

10.11591/ijict.v14i1.pp11-19
Niaz Ashraf Khan , Md. Ferdous Bin Hafiz , Md. Aktaruzzaman Pramanik
Predictive modelling plays a crucial role in healthcare, particularly in forecasting mortality due to heart failure. This study focuses on enhancing predictive modelling and interpretability in heart failure prediction through advanced boosting algorithms, ensemble methods, and SHapley Additive exPlanations (SHAP) analysis. Leveraging a dataset of patients diagnosed with cardiovascular diseases (CVD), we employed techniques such as synthetic minority over-sampling technique (SMOTE) and bootstrapping to address class imbalance. Our results demonstrated exceptional predictive performance, with the gradient boosting (GBoost) model achieving the highest accuracy of 91.39%. Ensemble techniques further enhanced performance, with the voting classifier (VC), stacking classifier (SC), and Blending achieving accuracies of 91.00%. SHAP analysis uncovered key features such as time, Serum_creatinine, and Ejection_fraction, significantly impacting mortality prediction. These findings highlight the importance of transparent and interpretable machine learning models in healthcare decision-making processes, facilitating informed interventions and personalized treatment strategies for heart failure patients.
Volume: 14
Issue: 1
Page: 11-19
Publish at: 2025-04-01

Explainable zero-shot learning and transfer learning for real time Indian healthcare

10.11591/ijict.v14i1.pp91-101
Swati Saigaonkar , Vaibhav Narawade
Clinical note research is globally recognized, but work on real-time data, particularly from India, is still lagging. This study initiated by training models on medical information mart for intensive care (MIMIC) clinical notes, focusing on conditions like chronic kidney disease (CKD), myocardial infarction (MI), and asthma using the structured medical domain bidirectional encoder representations from transformers (SMDBERT) model. Subsequently, these models were applied to an Indian dataset obtained from two hospitals. The key difference between publicly available datasets and real-time data lies in the prevalence of certain diseases. For example, in a real-time setting, tuberculosis may exist, but the MIMIC dataset lacks corresponding clinical notes. Thus, an innovative approach was developed by combining a fine-tuned SMDBERT model with a customized zero-shot learning method to effectively analyze tuberculosis-related clinical notes. Another research gap is the lack of explainability because deep learning (DL) models are inherently black-box. To further strengthen the reliability of the models, local interpretable model-agnostic explanations (LIME) and shapley additive explanations (SHAP) explanations were projected along with narrative explanations which generated explanations in a natural language format. Thus, the research provides a significant contribution with ensemble technique of zero-shot learning and SMDBERT model with an accuracy of 0.92 as against the specialized models like scientific BERT (SCIBERT), biomedical BERT (BIOBERT) and clinical BioBERT.
Volume: 14
Issue: 1
Page: 91-101
Publish at: 2025-04-01

Data analysis and visualization on titanic and student’s performance datasets-an exploratory study

10.11591/ijict.v14i1.pp68-76
Seong-Cheol Kim , Surender Reddy Salkuti , Alka Manvayalar Suresh , Madhu Sree Sankaran
Exploratory data analysis (EDA) is all about exploring the data in order to identify any underlying pattern before you try to use it to make a predictive model. It also plays a major role in the data discovery process as it is used to analyze data and to recapitulate their different characteristics, which is displayed efficiently with the help of data visualization methods. This paper aims to identify errors in the dataset, to understand the existing hidden structure and to identify new ones, to detect points in a dataset that deviate to a greater extent from the collected data (outliers), and also to find any relationship or intersection between the variables and constants. Two datasets are used namely ‘Titanic’ and ‘student’s performance’ to perform data analysis and ‘data visualization’ to depict ‘exploratory data analysis’ which acts as an important set of tools for recognizing a qualitative understanding. The datasets were explored and hence it assisted with identifying patterns, outliers, corrupt data, and discovering the relationship between the fields in the dataset.
Volume: 14
Issue: 1
Page: 68-76
Publish at: 2025-04-01

Depression detection through transformers-based emotion recognition in multivariate time series facial data

10.11591/ijai.v14.i2.pp1302-1310
Kenjovan Nanggala , Gregorius Natanael Elwirehardja , Bens Pardamean
Globally, the prevalence of mental health disorders, particularly depression, has become a pressing issue. Early detection and intervention are vital to mitigate the profound impact of depression on individuals and society. Leveraging transformer models, renowned for their excellence in natural language processing and time series tasks, we explore their application in depression detection using multivariate time series (MTS) data from facial expressions. Transformer models excel in sequential data processing but remain relatively unexplored in facial expression analysis. This study aims to compare transformer models applied to first-order time derivative data with traditional methods. We use the distress analysis interview corpus wizard of oz (DAIC-WOZ) dataset and evaluate models with mean absolute error (MAE) and root mean squared error (RMSE) metrics. Results show that transformer models on first derivatives outperform others with an MAE of 4.42 and RMSE of 5.42. While transformer models on raw data surpass XGBoost in RMSE, they fall short of LSTM+transformer with an MAE of 5.41 and RMSE of 6.02. Preprocessing through differentiation enhances transformer models' ability to capture temporal patterns, promising improved depression detection accuracy.
Volume: 14
Issue: 2
Page: 1302-1310
Publish at: 2025-04-01

Connected caregiving: investigating mothers in the era of digital access

10.11591/ijict.v14i1.pp347-354
Anissa Saidi , Wong Yee Von , Tirzah Zubeidah Zachariah@ Omar , Lim Seong Pek , Rita Wong Mee Mee , Khoo Kim Leng
Mothers have embraced and utilized digital access for nurturing and personal use to enhance their roles while balancing newfound demands. The Internet has provided mothers access to information on various topics, including pregnancy, childbirth, and infant care. Social media tools and platforms have also provided mothers with a space to connect with other mothers, share experiences, and seek support. This scoping review aims to identify the relationship of the focus skills among mothers in utilizing digital access. Four databases, including Scopus, web of science (WOS), education resources information centre (ERIC), and ScienceDirect, were used in this research, which found 36 articles for eligibility. Only 16 articles are eligible for analysis and reference after the exclusion and inclusion process for data collection. Based on the 16 publications examining mothers’ use of internet access, four essential skills have been identified. These included social, digital, cultural, and problem-solving skills and are acknowledged as being related to digital access mothering. The findings show these skills are offered to mothers through digital access, fostering diverse skill sets, contributing to their empowerment, and supporting sustainable development goal 5: gender equality, aiming to enhance women’s roles and ensure equal opportunities through digital inclusion.
Volume: 14
Issue: 1
Page: 347-354
Publish at: 2025-04-01

A survey on novel approach to semantic computing for domain specific multi-lingual man-machine interaction

10.11591/ijict.v14i1.pp1-10
Anjali Bohra , Nemi Chand Barwar
Natural language processing (NLP) helps computational linguists to understand, process, and extract information from natural languages. Linguist Panini signifies ’information coding’ in a language and explains that Karakas are semanticosyntactic relations between nouns and verbs that resemble participant roles of modern case grammar. Computational grammar maps vibhakti (inflections) of nominals and verbs to their participant roles. Karaka’s theory extracts semantic roles in the sentences which act as intermediate steps for various NLP tasks. The survey shows that NLP seeks to bridge the gap for man-machine interaction. The work presents the impact of machine learning on natural language processing with changing trends from traditional to modern scenarios with Panini’s classification scheme for semantic computing facilitating machine understanding. The study presents the significance of Karaka for semantic computing, methodologies for extracting semantic roles, and analysis of various deep learning-based language processing systems for applications like question answering. The survey covered around 50 research articles and 21 Karaka-based NLP systems performing multiple tasks like machine translation, question-answering systems, and text summaries using machine learning tools and frameworks. The work includes surveys from renowned journals, books, and relevant conferences, as well as descriptions of the latest trends and technologies in the machine learning domain.
Volume: 14
Issue: 1
Page: 1-10
Publish at: 2025-04-01

Heart disease approach using modified random forest and particle swarm optimization

10.11591/ijai.v14.i2.pp1242-1251
Khalidou Abdoulaye Barry , Youness Manzali , Rachid Flouchi , Mohamed Elfar
For the past two decades, heart disease has been classified as one of the main causes of mortality globally. Fortunately, most researchers focused on data mining techniques, which play an important role in accurately predicting heart disease to develop their models. In this paper, by combining particle swarm optimization (PSO) and modified random forest (MRF), a new approach (PSO-MRF) is proposed to predict heart disease. The main purpose is to select the important features after the bootstrap method for each decision tree in the random forest, and then optimize the MRF by the PSO algorithm. The experiments are carried out using the publicly accessible UCI heart disease datasets. Thorough experimental analysis demonstrates that our approach has outperformed the random forest algorithm as well as many other classifiers. This model helps doctors and researchers improve the diagnosis and treatment of heart disease, resulting in more prompt, accurate patient care.
Volume: 14
Issue: 2
Page: 1242-1251
Publish at: 2025-04-01

Cost-effective IoT-based automated vehicle headlight control system: design and implementation

10.11591/ijict.v14i1.pp325-333
Momotaz Begum , Nayeem Ullah , Mehedi Hasan Shuvo , Towhidul Islam , Thofazzol Hossen , Jia Uddin
The current world would be difficult without vehicles, which offer vital advantages for social connectivity, mobility, and technical advancement. Though motor vehicles provide benefits to passenger transportation, they also present certain challenges in their use. A major issue is nighttime traffic accidents caused by headlamps from automobiles traveling in reverse directions, that's why there is a high probability of accidents due to the glare on the driver's eyes. The phrase "Troxler effect" refers to an unexpected glare that a motorist recognizes. In this paper, we will provide an optimal solution to this challenge/Troxler effect. The primary objective of this paper is to design an internet of things (IoT)-based smart headlight control model. Our system introduced a cost-effective vehicle’s headlights controlled by light detection. According to this paper, a vehicle’s headlights are automatically rotated down when the sensor detects lights from the opposite direction of the vehicle headlights. We tried to reduce the road accident rate with our proposed system. This type of technology will prove useful in the motor vehicle sector and offer an innovative approach that ensures driver safety as well as increasing economic development.
Volume: 14
Issue: 1
Page: 325-333
Publish at: 2025-04-01

A hybrid machine learning approach for improved ponzi scheme detection using advanced feature engineering

10.11591/ijict.v14i1.pp50-58
Fahad Hossain , Mehedi Hasan Shuvo , Jia Uddin
Ponzi schemes deceive investors with promises of high returns, relying on funds from new investors to pay earlier ones, creating a misleading appearance of profitability. These schemes are inherently unsustainable, collapsing when new investments wane, leading to significant financial losses. Many researchers have focused on detecting such schemes, but challenges remain due to their evolving nature. This study proposes a novel hybrid machine-learning approach to enhance Ponzi scheme detection. Initially, we train an XGBoost classifier and extract its features. Meanwhile, we tokenize opcode sequences, train a gated recurrent unit (GRU) model on these sequences, and extract features from the GRU. By concatenating the features from the XGBoost classifier and the GRU, we train a final XGBoost model on this combined feature set. Our methodology, leveraging advanced feature engineering and hybrid modeling, achieves a detection accuracy of 96.57%. This approach demonstrates the efficacy of combining XGBoost and GRU models, along with sophisticated feature engineering, in identifying fraudulent activities in Ethereum smart contracts. The results highlight the potential of this hybrid model to offer more robust and accurate Ponzi scheme detection, addressing the limitations of previous methods.
Volume: 14
Issue: 1
Page: 50-58
Publish at: 2025-04-01

Evaluating ChatGPT’s Mandarin “yue” pronunciation system in language learning

10.11591/ijai.v14.i2.pp1634-1641
Yoke Lian Lau , Swee Mee Tan , Anna Lynn Abu Bakar , Zi Xian Yong , Zi Hong Yong , Ernahwatikah Nasir , Chen Jung Ku
By incorporating voice control technology into ChatGPT, it becomes possible to engage in conversations or dialogues with individuals who are actively engaged in the process of acquiring language skills. Our study team conducted a modest experiment to evaluate the efficacy of a voice control feedback system in facilitating the mastery of the most challenging pronunciation of the Mandarin syllable "yue". The objective of this study is to evaluate the effectiveness of voice-controlled ChatGPT in aiding learners to acquire accurate pronunciation of the Mandarin phoneme "yue". Furthermore, the study seeks to investigate the methods utilised by the ChatGPT model in identifying and distinguishing the word "yue" when it is used alone or in combination with "ye" and "yi". We employed many testing approaches, including single-word instances, paired instances, and the integration of phrases. In addition, we evaluated the model's ability to accurately detect the term "yue" in short sentences and, ultimately, in a longer sentence.
Volume: 14
Issue: 2
Page: 1634-1641
Publish at: 2025-04-01

Managing cyber resilience literacy for consumers

10.11591/ijict.v14i1.pp122-131
Tatiana Antipova , Simona Riurean
It seems inevitable that digitalization will have a profound and irreversible impact on our lives, and it seems reasonable to suppose that our world will never be the same again. Objectives of this study is to gain insight into consumers’ understanding of cyber security threats and their willingness to enhance their cyber resilience. To achieve this, a survey was conducted using AI tools such as Open ChatGPT, Copilot and PI. The survey was distributed selectively among consumers via Google Form. The results of the survey conducted during the study indicated that the majority of respondents (72%) expressed interest in attending online interactive seminars to gain more knowledge about managing cybersecurity threats. However, respondents with the lowest cyber resilience knowledge did not express the same level of interest. With technology becoming an increasingly important aspect in our everyday lives, it is becoming ever clearer that cybersecurity posture relies on the behavior consumers and organizations. Based on the rule that ‘never trust, always verify’ we designed ‘cybersecurity zero-trust framework model’ for consumers that allows them to protect themselves against cybersecurity threats. In an ever-shifting landscape of cybersecurity, it is important to recognize the value of continuous education as a necessity, not just an option.
Volume: 14
Issue: 1
Page: 122-131
Publish at: 2025-04-01

Collaborative singular value decomposition with user-item interaction expansion for first-time user and item recommendations

10.11591/ijict.v14i1.pp111-121
Manal Loukili , Fayçal Messaoudi
In today's digital landscape, recommendation systems are essential for delivering personalized content and improving user engagement across various platforms. However, a key challenge known as the cold-start problem—where limited user-item interaction data hampers the ability to generate accurate recommendations—remains a significant obstacle, particularly for new users and items. To address this issue, this paper introduces an enhanced methodology combining collaborative singular value decomposition (Co-SVD) with an innovative approach to reduce data sparsity. The objective of this research is to improve recommendation accuracy in sparse data environments by leveraging collaborative information in the user-item interaction matrix. Extensive experiments conducted on an e-commerce dataset validate the superiority of the proposed Enhanced Co-SVD model over traditional Co-SVD, content-based filtering, and random recommendation methods across multiple metrics. Our approach demonstrates particular strength in cold-start scenarios, providing precise recommendations with minimal user interaction data. These findings have important implications for e-marketing, personalized user experiences, and overall business success in online environments.
Volume: 14
Issue: 1
Page: 111-121
Publish at: 2025-04-01

Graph-based methods for transaction databases: a comparative study

10.11591/ijai.v14.i2.pp1663-1672
Wael Ahmad AlZoubi , Ibrahim Mahmoud Alturani , Roba Mahmoud Ali Aloglah
There has been an increased demand for structured data mining. Graphs are among the most extensively researched data structures in discrete mathematics and computer science. Thus, it should come as no surprise that graph-based data mining has gained popularity in recent years. Graph-based methods for a transaction database are necessary to transform all the information into a graph form to conveniently extract more valuable information to improve the decision-making process. Graph-based data mining can reveal and measure process insights in a detailed structural comparison strategy that is ready for further analysis without the loss of significant details. This paper analyzes the similarities and differences among four of the most popular graph-based methods that is applied to mine rules from transaction databases by abstracting them out as a concrete high-level interface and connecting them into a common space.
Volume: 14
Issue: 2
Page: 1663-1672
Publish at: 2025-04-01

Enhancing facial recognition accuracy through feature extractions and artificial neural networks

10.11591/ijai.v14.i2.pp1056-1066
Adhi Kusnadi , Ivranza Zuhdi Pane , Fenina Adline Twince Tobing
Facial recognition is a biometric system used to identify individuals through faces. Although this technology has many advantages, it still faces several challenges. One of the main challenges is that the level of accuracy has yet to reach its maximum potential. This research aims to improve facial recognition performance by applying the discrete cosine transform (DCT) and Gaussian mixture model (GMM), which are then trained with backward propagation of errors (backpropagation) and convolutional neural networks (CNN). The research results show low DCT and GMM feature extraction accuracy with backpropagation of 4.88%. However, the combination of DCT, GMM, and CNN feature extraction produces an accuracy of up to 98.2% and a training time of 360 seconds on the Olivetti Research Laboratory (ORL) dataset, an accuracy of 98.9% and a training time of 1210 seconds on the Yale dataset, and 100% accuracy and training time 1749 seconds on the Japanese female facial expression (JAFFE) dataset. This improvement is due to the combination of DCT, GMM, and CNN's ability to remove noise and study images accurately. This research is expected to significantly contribute to overcoming accuracy challenges and increasing the flexibility of facial recognition systems in various practical situations, as well as the potential to improve security and reliability in security and biometrics.
Volume: 14
Issue: 2
Page: 1056-1066
Publish at: 2025-04-01
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