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31,042 Article Results

Enhancing communication and interaction in the movie industry based SparkMLlib's recommendation system

10.11591/ijai.v14.i6.pp4661-4674
Said Chakouk , Abdelkerim Zitouni , Nazif Tchagafo , Ahiod Belaid
In the ever-evolving landscape of streaming platforms, recommendation systems contribute significantly to enhancing the user experience. This article examines the significance of these systems in suggesting movies, analyzing their impact on user satisfaction and platform performance. Utilizing SparkMLlib, a powerful tool for large-scale data processing, we explore various recommendation techniques, including collaborative filtering and content-based filtering. We highlight the dimension of digital communication to further enhance the accuracy of recommendations and foster greater user engagement. Our study also addresses the challenges and future opportunities related to recommendation systems, emphasizing the need for transparency and ethical algorithms. This research highlights the potential for recommendation systems to revolutionize the digital entertainment landscape and shape the future of the movie industry.
Volume: 14
Issue: 6
Page: 4661-4674
Publish at: 2025-12-01

Combining convolutional operators in unsupervised networks for kidney abnormalities

10.11591/ijai.v14.i6.pp4541-4551
Aekkarat Suksukont , Anuruk Prommakhot , Jakkree Srinonchat
Deep learning plays a pivotal role in advancing the diagnosis of renal dysfunction, achieving performance levels comparable to those of medical experts. However, disease domain variations and model differences can impact learning quality. To address renal dysfunction, we propose dual stream convolutional (DSC) and dual-input convolutional (DIC) for unsupervised learning. The proposed network is designed to process multi scale data and employs parallel data aggregation to enhance learning capabilities, improving the reliability of the experimental results. DSC achieved training losses of 0.0069, 0.0056, 0.0042, and 0.0048 for normal, cyst, stone, and tumor datasets, respectively, while DIC achieved losses of 0.0066, 0.0063, 0.0044, and 0.0058 for the same categories. The experimental results demonstrate that our proposed models outperform state of-the-art approaches, making them well-suited for broad application in clinical research studies.
Volume: 14
Issue: 6
Page: 4541-4551
Publish at: 2025-12-01

Applied differential comparative study of VANET simulators: TrAD protocol study using veins and VNS VANET simulators in both real and standard city maps

10.11591/ijeecs.v40.i3.pp1357-1367
Amina Sedjelmaci , Hayet Benosman , Ahmed Abdul Rahuman
This study presents a comprehensive evaluation of vehicular ad-hoc networks (VANETs) by analysing the performance of two leading simulation frameworks: VEINS and VNS. With the increasing demand for efficient vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, understanding the capabilities of data dissemination protocols is crucial for enhancing traffic safety and optimizing route management. We investigate the traffic adaptive data (TrAD) protocol, which dynamically adapts to real-time traffic conditions to ensure reliable communication in high-density vehicular scenarios. Simulations were conducted using OMNeT++ with VEINS and NS-3 with VNS across urban environments in Manhattan and Tlemcen, evaluating TrAD’s effectiveness under diverse traffic conditions. The findings offer valuable insights into the operational strengths of the two simulation frameworks and their implications for advancing vehicular communication systems. This work contributes to the development of robust VANET protocols, supporting innovations in smart and sustainable transportation systems.
Volume: 40
Issue: 3
Page: 1357-1367
Publish at: 2025-12-01

Metaheuristic optimization for sarcasm detection in social media with embedding and padding techniques

10.11591/ijai.v14.i6.pp5027-5037
Geeta Sahu , Manoj Hudnunkar
Sarcasm is a sophisticated mode of expression that allows speakers to express their opinions subtly. Stakeholders provide unstructured messages with extended phrases, making it difficult for computers and people to understand. This research aims to develop a sarcasm detection method to identify words in phrases as sarcastic or non-sarcastic from text, utilizing natural language processing appliances. The first step is pre-processing, when the padding and embedding are performed. Zero padding and end padding are used for the padding. At the same time, different embedding techniques, such as word2vec, Glove, and BERT, are used. Following pre-processing, the features are extracted from the pre-processed data, including "information gain, chi-square, mutual information, and symmetrical uncertainty-based features." Then, a hybrid optimization technique known as clan-updated grey wolf optimization (CU-GWO) is used for optimized features and weight selection. An ensemble technique was applied to extract optimal features. The classifiers in the proposed suggested ensemble technique with deep convolution neural network (DCNN). DCNN offers fine weight tuning and detection results.The performance analysis and its impact on the proposed model for sarcasm detection are classified with good accuracy into sarcastic and non-sarcastic categories. The results are also compared with against those of the GloVe and BERT techniques.
Volume: 14
Issue: 6
Page: 5027-5037
Publish at: 2025-12-01

Spam social media profile detection using hybrid positive unlabelled learning

10.11591/ijai.v14.i6.pp4838-4847
Nidhi A. Patel , Nirali Nanavati
Online social networks (OSNs) are a communication medium of social interaction for people, where social activities, entertainment, business oriented activities, and information are exchanged. It creates an environment with worldwide connectivity where groups of individuals may discuss their interests and activities on social media platforms. Billions of people routinely interact with social content, opinion sharing, recommendations, networking, scouting, social campaigns, alerting on OSNs. The increase in popularity of OSNs creates new challenges and perspectives to the researchers of social networks, which is of interest in various fields. One of the most popular networking platforms for microblogging is X (formerly Twitter). Millions of spam accounts have inundated the X network, which could damage normal users' security and privacy. Hence, the research in this filed has become essential for enhancing real users' protection and identifying spam profiles. In this manuscript, we propose hybrid approach based on semi-supervised learning to detect the spam profiles. The proposed work is based on the positive and unlabeled (PU) learning algorithm, which learns from an unlabeled dataset and a small number of positive instances. Simulation results demonstrate that our approach outperformed existing PU learning approach by 17.39% and 17.51% improvement respectively in spam detection rate on X and Instagram datasets.
Volume: 14
Issue: 6
Page: 4838-4847
Publish at: 2025-12-01

Early detection of tar spot disease in Zea mays using hyperspectral reflectance and machine learning

10.11591/ijai.v14.i6.pp4722-4730
Claudia Nohemy Montoya-Estrada , Oscar Cardona-Morales , Oscar López-Naranjo , Freddy Eliseo Hernandez-Jorge , Yeison Alberto Garcés-Gómez
Ensuring food security and meeting the economic needs of farmers and nations depend heavily on detecting and preventing crop yield losses. Early detection of tar spot caused by Phyllachora maydis is crucial to implementing efficient mitigation actions in the earliest stages of infestation. Currently, visual methods are used for detection, which require extensive training and experience from the operator. However, remote sensing techniques can be used to detect tar spot infestation through the selection of wavelengths present in the maize plant spectral signature. This research proposes using machine learning techniques and logistic regression to determine the first stage of tar spot infestation. The results show that the logistic regression model is the most suitable for detecting this first stage, and the K-Nearest Neighbors Classification and Random Forest Classification algorithms generate the best classification results. This approach can significantly reduce costs in terms of time, labor, and subjective analysis.
Volume: 14
Issue: 6
Page: 4722-4730
Publish at: 2025-12-01

Predicting the severity of road traffic accidents Morocco: a supervised machine learning approach

10.11591/ijai.v14.i6.pp4461-4473
Halima Drissi Touzani , Sanaa Faquir , Ali Yahyaouy
Early prediction of road accidents fatality and injuries severity is one of the important subjects to road safety emphasizing the critical need to prevent serious consequences to reduce injuries and fatalities. This study uses real road accidents data set in Morocco. It represents the intersection between road safety and data science, aiming to employ machine learning techniques to provide valuable insights in accident’s severity prevention. The purpose of this paper is to study road accidents data in the country and combine results from statistical methods, spatial analysis, and machine learning models to determine which factors will mostly contribute to increase the accident’ severity in the country. A comparison of results obtained was also conducted in this paper using different metrics to evaluate the effectiveness of each method and determine the most important factors that contribute to increase the fatality or injuries severity in the specific context of accidents. The best prediction model was then injected into a proposed algorithm where more intelligent techniques are included to be implemented in a car engine to perform an early detection of severe accidents and therefore preventing crashes from happening.
Volume: 14
Issue: 6
Page: 4461-4473
Publish at: 2025-12-01

Transformer and text augmentation for tourism aspect-based sentiment analysis

10.11591/ijai.v14.i6.pp4614-4622
Samuel Situmeang , Sarah Rosdiana Tambunan , Jevania Jevania , Mastawila Febryanti Simanjuntak , Sandro Sinaga
The 36.98% growth in the quantity of electronic word of mouth (e-WOM) over the past five years presents opportunities for the tourism industry to understand tourists' needs and desires better when analyzed effectively. Aspect-based sentiment analysis (ABSA) is proposed as a solution, as it can identify the sentiment at a more detailed aspect level. Prior research revealed two issues in ABSA: imbalanced datasets and poor performance in representing implicit aspects and opinions. The authors proposed a combination of the bidirectional and auto-regressive transformer (BART) and bidirectional encoder representations from transformers (BERT) models. Leveraging BART capability in modeling context and BERT expertise in modeling text semantics and nuances, the author proposed an ABSA model that combines BART and BERT using the ensemble method. The experimental results reveal that combining these models significantly enhances the performance of the ABSA model, with an F1-score reaching 70%. Furthermore, text augmentation and preprocessing did not bring improvements in ABSA performance.
Volume: 14
Issue: 6
Page: 4614-4622
Publish at: 2025-12-01

A merchant analytics framework for revenue forecasting and financial stress detection using transaction data

10.11591/ijai.v14.i6.pp4848-4864
Yara Harb , Wissam Baaklini , Nadine Abbas , Seifedine Kadry
By processing payments and providing specialized financial services, acquiring banks are essential for merchants’ operations. To forecast 30-day revenue trajectories, identify seasonal demand patterns, and identify early indicators of financial stress, this paper presents a scalable merchant analytics framework that benefits from transactional data. The framework captures multi-level seasonalities using Prophet time series model, allowing dynamic product offerings like revenue-based loans. Proactive risk management is supported offerings like revenue-based loans. Proactive risk management is supported. by a new stress-flagging mechanism that identifies merchants at risk based on deviations in revenue trends. The framework achieved a median 30-day mean absolute percentage error (MAPE) of 56.51% after the validation on a dataset with 130,350 transactions from 460 merchants in a volatile economic environment. The model demonstrated significant practical utility in identifying financial distress and segmenting merchant behavior, despite its moderate predictive precision, which is common challenge in high-variance merchant datasets. Model outputs are converted into decision-support visualizations along with an interactive dashboard.
Volume: 14
Issue: 6
Page: 4848-4864
Publish at: 2025-12-01

A smart grid fault detection using neuro-fuzzy deep learning algorithm

10.11591/ijai.v14.i6.pp5096-5105
Etienne Francois Mouckomey , Jacques Bikai , Camille Franklin Mbey , Alexandre Teplaira Boum , Felix Ghislain Yem Souhe , Vinny Junior Foba Kakeu
This paper proposes a novel data analysis framework that integrates deep learning with a binary neuro-fuzzy algorithm to address the problem of fault localization in smart power grids. In the first stage, a long short-term memory (LSTM) network is employed to train data samples collected from smart meters. The resulting learned features are subsequently utilized by an adaptive neuro-fuzzy inference system (ANFIS) for accurate fault detection and classification. Through this intelligent hybrid approach, multi-phase faults can be efficiently identified using a limited amount of data. The proposed method distinguishes itself by its capacity to rapidly train and test large datasets while maintaining high computational efficiency. To evaluate the performance of the model, an advanced simulation of the IEEE 123-node test feeder is conducted. The robustness and effectiveness of the proposed framework are validated using multiple performance metrics, including precision, recall, accuracy, F1-score, computational complexity, and the ROC curve. The results demonstrate that the proposed deep learning–based model significantly outperforms existing approaches in the literature, achieving a fault detection and classification precision of 99.99%.
Volume: 14
Issue: 6
Page: 5096-5105
Publish at: 2025-12-01

Fine-tuning multilingual transformers for Hinglish sentiment analysis: a comparative evaluation with BiLSTM

10.11591/ijai.v14.i6.pp4684-4693
Jyoti S. Verma , Jaimin N. Undavia
Growing trend of code-mixing in languages, in the form of Hinglish, greatly tests the skills of conventional sentiment analysis tools. The research contributes a fine-tuned multilingual transformer model built exclusively for classifying sentiment of Hinglish customer reviews. Drawing from pre trained BERT-base-multilingual-case architecture, the model gets transformed with the process of fine-tuning the same on synthetically prepared and balanced dataset simulating positive, negative, and neutral sentiments. Sophisticated methods like focal loss for addressing the class imbalance and mixed precision training for maximization of computational effectiveness are embedded within the training process. Experimental results suggest that the proposed method significantly captures the fine-grained linguistic patterns of code-mixed text, improving sentiment classification accuracy. The results show promising potential for enhancing customer feedback analysis in e-commerce, social media monitoring, and customer support use cases, where it is crucial to comprehend the sentiment behind code-mixed reviews.
Volume: 14
Issue: 6
Page: 4684-4693
Publish at: 2025-12-01

Deep learning-based evaluation for distributed denial of service attacks detection

10.11591/ijai.v14.i6.pp4982-4992
Neethu S. , H. V. Ravish Aradhya , Viswavardhan Reddy Karna
Software-defined network (SDN) introduces a programmable and centralized control mechanism for managing network infrastructure, enhancing flexibility and efficiency. However, this architecture is prone to security threats, particularly distributed denial of service (DDoS) attacks that exploit centralized control. This study presents a comparative analysis of several deep learning (DL) models—namely, multilayer perceptron (MLP), artificial neural network (ANN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM)—for detecting DDoS threats within SDN environments. The research incorporates key preprocessing techniques such as feature selection and synthetic minority oversampling technique (SMOTE) to handle class imbalance. The results indicate that sequence-aware models like LSTM and RNN are highly effective in interpreting temporal network behavior, with LSTM achieving the highest performance (accuracy: 91%, precision: 86%, recall: 94%, and F1-score: 90%). These findings underscore the potential of advanced DL methods in fortifying SDN infrastructures against complex cyber threats.
Volume: 14
Issue: 6
Page: 4982-4992
Publish at: 2025-12-01

Low-speed scalar control of induction motor by fuzzy logic

10.11591/ijai.v14.i6.pp4623-4635
Alfonso Alejandro Sevilla-Hidalgo , Rossy Uscamaita-Quispetupa , Julio Cesar Herrera-Levano , Limberg Walter Utrilla Mego , Roger Jesus Coaquira-Castillo
Efforts have continually been directed toward optimizing processes in various fields, and the application in induction motors is no exception. Scalar control V/f offers a straightforward method to regulate the speed of a three-phase induction motor (TIM). However, it faces challenges at low speeds or proportionally at low frequencies, often failing to operate below 20% of its rated speed. This control typically pairs with a PI controller (PIC) for closed loop speed regulation, but its limited control range hinders performance at low speeds. Although intelligent methods have been developed to improve scalar V/f control, attention is often focused on high speeds, while control at low speeds is overlooked. This paper presents the simulation of a fuzzy controller (FC) with a Mamdani-type structure designed to achieve effective low-speed control of a TIM using the V/f scalar control technique. The results not only show improvements in overshoot and settling time but also reveal that the FC can control speeds as low as 6.06% of the rated speed, and it ensures a starting current below the designed motor current under load. Comparative analysis indicates that the FC outperforms the PIC in low-speed control, and it provides an optimal inrush current across different low speeds.
Volume: 14
Issue: 6
Page: 4623-4635
Publish at: 2025-12-01

Humans’ psychological traits classification from their spending categories using artificial intelligence algorithms

10.11591/ijai.v14.i6.pp4552-4564
Arpitha Chikkamagaluru Narasimhe Gowda , Sunitha Madasi Ramachandra
The analysis of human behavior data generated by digital technologies has gained increasing attention in recent years. Spending categories form a significant part of this digital footprint. In this study, we investigate the degree to which human expenditure records can be used to infer psychological traits from transaction data. A broad feature space was constructed, consisting of overall spending behavior, category-related spending behavior, and customer category profiles. These features were examined to identify their correlations with the Big Five personality traits. A dataset containing over 1,200 users’ transaction histories over three months was obtained from Kaggle. Personality trait labels were derived using a percentile-based classification method. Multiple AI algorithms: decision tree (DT), random forest (RF), logistic regression (LR), and support vector machine (SVM) were employed, along with a convolutional neural network (CNN) to classify personality traits. The CNN model, incorporating multi-dimensional convolutional layers and the full feature space, achieved a high accuracy of 99.03%. The outcomes of the experiment indicate the efficiency of combining behavioral features and AI models in psychological trait classification. The study also highlights ethical considerations, including privacy risks and misuse of inferred personality details.
Volume: 14
Issue: 6
Page: 4552-4564
Publish at: 2025-12-01

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

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