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31,456 Article Results

Convolutional neural network for speech emotion recognition in the Moroccan Arabic dialect language

10.11591/ijeecs.v37.i3.pp1588-1595
Soufiyan Ouali , Said El Garouani
Extracting the speaker's emotional state has become an active research topic lately due to the demand for more human interactive applications. This field of research has noted significant advancement, especially in the English language, owing to the availability of massive speech-labeled corpora. However, the progress of analogous methodologies in the Arabic language is still in its infancy stages. This paper presents a new massive natural speech emotion dataset and a speech recognition model for the Moroccan Arabic language. Four primary emotion labels were selected: happy, sad, angry, and neutral. Various spectral features, such as the mel-frequency cepstral coefficient (MFCC), were extracted and tested to determine the optimal feature combination. A convolutional neural networks (CNNs) model was built and trained on our dataset. The results were compared between spectral features individually and combined with the CNN model resulting in the selection of MFCC, root-mean-square (RMS), mel-scaled spectrogram, and spectral, as optimal spectral features for our dataset. This selection yielded significant results, with an accuracy of 99.55% for emotion recognition, outperforming the existing research.
Volume: 37
Issue: 3
Page: 1588-1595
Publish at: 2025-03-01

Classification of traditional Joha rice using deep neural network

10.11591/ijeecs.v37.i3.pp1682-1691
Hemanta Kalita , Mirzanur Rahman
Rice is the major food for the most of the people in the world. It is grown comfortably in a rain fed area. Rice is called a kharif crop in India. According to the International Rice Research Institute (IRRI) classification rice is classified as Ahu, Sali, Boro and Hill rice. In north eastern India the Joha rice is mainly cultivated in the state of Assam. There are many varieties of Joha rice like Kunkuni, Rampal, Manipuri, Tulshi, and Keteki. But it is quite complex for the farmers as well as the common people to differentiate these type of Joha rice due to their morphological structure. Initially this type of classification done by naked eyes or used some laboratory experiments. But due to the tiredness and some external factors it is not fruitful. Some of the image classification technologies are used in classification as well identification of rice in different research work using MATLAB. At present deep learning plays a tremendous role in image analysis in agricultural domain. Here in this paper we take two verities of Joha rice which are closer in morphological structure and cannot be separated by our naked eyes. Using deep neural network, binary classification is done in these Joha rice.
Volume: 37
Issue: 3
Page: 1682-1691
Publish at: 2025-03-01

A reliable and secure demand side control for an IoT-enabled smart power system using machine learning

10.11591/ijeecs.v37.i3.pp1428-1434
Vemulapalli Harika , Gudavalli Madhavi , Hanumantha Rao Battu , Rambabu Kasukurthi , Pradeep Jangir , John T Mesia Dhas
As the adoption of IoT-enabled smart power systems grows, the necessity for reliable and secure demand-side control becomes paramount. This paper introduces a robust demand-side management (DSM) engine that leverages machine learning to enhance both the reliability and security of smart grids. This paper presents a novel demand-side control system leveraging advanced machine learning techniques to optimize energy usage in smart power systems. The proposed system integrates IoT devices for data acquisition and employs machine learning algorithms to forecast energy demand, detect anomalies, and enable adaptive control strategies. By harnessing predictive analytics, the system anticipates consumption patterns and adjusts power distribution to maintain stability and prevent overloads. Moreover, robust security protocols are incorporated to protect the system against cyber threats and unauthorized access, ensuring data integrity and user privacy. Extensive simulation results demonstrate the system’s efficacy in reducing energy wastage, improving load balancing, and enhancing the overall reliability of the power grid. This research underscores the potential of combining IoT and machine learning to develop resilient and secure energy management solutions, paving the way for more sustainable and smart power systems.
Volume: 37
Issue: 3
Page: 1428-1434
Publish at: 2025-03-01

Marrying deep learning within blockchain technology for credit card fraud prevention

10.11591/ijeecs.v37.i3.pp1985-1998
Imane Karkaba , El Mehdi Adnani , Mohammed Erritali
Over the last decade or so, an excessive turnout on e-financial transactions by companies and customers results in a pinnacle growth of credit card fraudulent acts, leading them to lose frequently huge amounts of money. In their trial to find the key to this issue, specialists and experts have founded a bunch of fraud detection and prevention models relied on data mining, machine learning and deep learning. Yet, the outcomes were not effective nor optimal. Thereupon, to foster these prototypes’ function, Blockchain -a safe, decentralized and unchangeable database- was deployed to ban any sort of anomaly or data alteration after storing. For identifying malicious financial behaviours, our work managed to intermingle a pre-designed deep learning prototype with Blockchain. That is to say -for the sake of preventing fraudulence that concerns credit card- we applied the former prototype in Blockchain system. Still, Blockchain showed impotence in terms of using off-chain data, which embeds deep learning pattern, specifically through smart contract. Hence, we activated chainlink boosting our model to surmount this obstacle.
Volume: 37
Issue: 3
Page: 1985-1998
Publish at: 2025-03-01

Neuro-adaptive hierarchical sliding mode control for a 3-wheeled mobile robot with disturbance rejection

10.11591/ijeecs.v37.i3.pp1515-1531
Jose Carlos Vizarreta Mamani , Bladimir Leonel Vera Huamani , Raul Sulla Torres
High tracking accuracy and fast convergence are essential features in the control of wheeled mobile robots for autonomous navigation applications. However, the design of a control system for these robots faces significant challenges due to the inherent complexity of their nonlinear dynamics and their non-holonomic underactuated nature. This article introduces a novel control framework for trajectory tracking of a three-wheeled mobile robot (3WMR), considering external and internal disturbances. To address the uncertain nonlinear and underactuated non-holonomic dynamics of the 3WMR, an adaptive hierarchical fast terminal sliding mode control (AHFTSMC) strategy is proposed. In this approach, an adaptive neural network scheme adjusts the sliding surface coefficients in real time to minimize tracking errors and mitigate the chattering phenomenon. In addition, a finite time disturbance observer (FTDO) is designed to accurately estimate and compensate for unknown lumped disturbances, which helps to improve the disturbance rejection capability. The stability of the closed-loop system is demonstrated using Lyapunov theory. The proposed control approach is validated by numerical simulations, and a comparative analysis of recent hierarchical sliding mode controllers (SMC) is performed. The results demonstrate that the proposed approach achieves superior performance in terms of fast convergence and tracking accuracy, which are crucial features for the autonomous navigation of mobile robots.
Volume: 37
Issue: 3
Page: 1515-1531
Publish at: 2025-03-01

Implementation and analysis of temperature and gas sensor datalogger in multi-stage condenser pyrolysis

10.11591/ijeecs.v37.i3.pp1497-1505
Muhammad Fathuddin Noor , Sumardi Hadi Sumarlan , Yusuf Hendrawan , Bambang Dwi Argo , Hartawan Abdillah
The development of alternative energy sources is crucial for addressing contemporary energy and environmental challenges. This study presents the implementation and analysis of a temperature and gas sensor datalogger within a multi-stage condenser pyrolysis system, designed to assess the potential of pyrolytic liquid smoke derived from Cerbera odollam (bintaro) fruit waste. The datalogger system was developed to continuously capture and retain data on temperature, air humidity, and non-condensable gases throughout the pyrolysis process. The experimental research focused on evaluating the impact of varying reactor temperatures (250 °C, 300 °C, and 350 °C) and cooling fluid flow rates on the performance of the condenser and the production of bio-oil. Results indicated that reactor temperature significantly affects bio-oil yield, with the highest output of 190 mL obtained at 350 °C. Additionally, the temperature of the smoke entering each condenser and the cooling water’s temperature were found to influence the composition of the condensates produced by each stage. This study highlights the importance of integrating sensor technologies to optimize pyrolysis conditions, thereby enhancing the efficiency of energy production from bintaro fruit waste.
Volume: 37
Issue: 3
Page: 1497-1505
Publish at: 2025-03-01

Predicting student status using machine learning by analyzing classroom behaviors with X-API data

10.11591/ijeecs.v37.i3.pp2069-2076
Abdelamine Elouafi , Ilyas Tammouch , Souad Eddarouich , Raja Touahni
We explore the emergence and growing significance of educational data mining, a field dedicated to extracting valuable insights from vast datasets gathered from diverse educational environments. Utilizing the experience API (XAPI) and the Kalboard 360 online learning platform, our research presents a novel behaviorally based student performance model that evaluates the influence of student interactions on academic results. We create reliable models for precisely projecting academic success by utilizing machine learning techniques including logistic regression, k-nearest neighbors (KNNs), support vector machines (SVM), decision trees, random forests (RF), and XGBoost. The outcomes show a notable increase in categorization accuracy. Through the personalization of instruction, formative assessment support, and proactive identification of each student's unique needs to maximize their learning experience, this approach holds the potential to improve educational processes.
Volume: 37
Issue: 3
Page: 2069-2076
Publish at: 2025-03-01

Implementation of perspective-n-point techniques and YOLOv5 algorithm based on surveillance camera for localization

10.11591/ijeecs.v37.i3.pp1744-1757
Siripong Pawako , Nopparut Khaewnak , Jiraphon Srisertpol
The technology of processors has advanced significantly, resulting in smaller and more powerful devices with much processing capability. Particularly, camera technology has witnessed extensive research in utilizing images for various applications. Currently, surveillance cameras are widely used for security purposes when abnormal events occur. In this research, the benefits of utilizing data from surveillance cameras are explored to assist in determining the position of a moving robot using the perspective-n-point (PnP) technique. the scale factor, which varies, has been improved by integrating checks with the YOLOv5 algorithm. This algorithm employs a custom model to specifically detect the robot of interest, enabling the determination of its real-world position using multiple surveillance cameras. These cameras have different perspectives within the same area. Considering the deviation caused by determining the position from a single viewpoint, multiple cameras are employed to mitigate this issue.
Volume: 37
Issue: 3
Page: 1744-1757
Publish at: 2025-03-01

Comparative analysis of edge detection image processing techniques for efficient traffic signal management system

10.11591/ijeecs.v37.i3.pp1555-1568
Niranjana Chandrasekara Bharathy , Manju Shivanna Dasappanavar , Anne Gowda Aleri Byre Gowda , Jijesh Jisha Janardhanan
Traffic monitoring and control pose significant challenges, particularly in metropolitan areas worldwide. Traditional methods such as timers, traffic police control, and sensor-based systems have become increasingly ineffective due to escalating traffic volumes. Image processing emerges as a promising technology for enhancing traffic control systems. This research aims to address the inefficiencies of current traffic management systems (TMS) by conducting a detailed comparative analysis of conventional image processing techniques, focusing on edge-based methods. Utilizing the open source computer vision (OpenCV) Library, we evaluated various edge detection techniques based on qualitative parameters like environmental lighting, object detection, and size, as well as quantitative parameters such as average traffic density, sharpness ratio (SR), abruptness, sensitivity factors (SF), processing time, frame processing time, and average frames per second (FPS). The study finds that the Canny edge detection technique outperforms others, with an average traffic density of 30.12 across 10 frames, superior SR, and minimal processing time of 99 seconds. This makes it highly effective for traffic control applications by generating high-quality edge maps with minimal noise. Our findings suggest that improving conventional image processing techniques and integrating deep learning algorithms can further enhance TMS, leading to more efficient urban mobility and reduced environmental impact.
Volume: 37
Issue: 3
Page: 1555-1568
Publish at: 2025-03-01

Medical image registration and classification using smell agent rat swarm optimization based deep Maxout network

10.11591/ijeecs.v37.i3.pp1908-1917
Sanjeevkumar Angadi , Mukesh Kumar Tripathi , Chudaman Devidasrao Sukte , Shivendra Shivendra
Medical image registration (MIR) is a crucial task in clinical image processing, involving the alignment of images from different modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), across various time points and subjects. Despite numerous advancements, no universal method caters to all MIR applications. This paper introduces the smell agent rat swarm optimization based deep Maxout network (SARSO-DMN) for MIR and classification. This work aims to enhance the accuracy and efficiency of medical image alignment and classification, addressing the challenges posed by diverse imaging modalities and temporal variations. The problem involves effectively registering CT and MRI images, followed by inhale and exhale classification. The proposed approach begins with feeding the input images into a convolutional neural network (CNN), followed by applying a deformation field to generate an intermediate output (output-1). This output, along with the input MRI images, is further processed by a CNN to produce output-2. Subsequently, output-2 and the input MRI image are subjected to another CNN, resulting in the final registered image. The classification phase utilizes a DMN optimized by the SARSO algorithm, which combines smell agent optimization (SAO) and rat swarm optimizer (RSO). The results demonstrate that SARSO-DMN achieves a maximum accuracy of 90.7%, a minimum false positive rate (FPR) of 11.3%, and a maximum true positive rate (TPR) of 91.2%. The SARSO-DMN approach provides a robust solution for MIR and classification, leveraging advanced optimization techniques to enhance performance.
Volume: 37
Issue: 3
Page: 1908-1917
Publish at: 2025-03-01

Linguistic feature selection for personality trait identification from textual data

10.11591/ijeecs.v37.i3.pp1976-1984
Angad Singh , Priti Maheshwary , Nitin Kumar Mishra , Timothy Malche
Personality identification is a common and central problem in text processing. Sensing personality is helpful for various purposes; for example, estimating users' personalities before providing them with any service is necessary. Individuality is essential in a person's nature in every outlook, for instance, in text writing. But, this remains a core challenge because of the low accuracy achieved. The proposed study solves this problem and presents a big five trait identification technique from text data, which applies a feature selection method to increase accuracy. This technique is called linguistic feature selection for personality trait identification (LFSPTI). This technique first finds features based on mutual information (MI), F-statistic, principal component analysis (PCA), and chi-square, then uses the genetic algorithm (GA) to select high-ranked features from all feature subsets. These four parameters provide various forms of the dataset. The experimental results exhibit that the LFSPTI method enhances the classification accuracy against the best of the competing methods by 1.18%, 0.83%, 1.61%., 1.15%, 1.82%, and 1.39% for extraversion (EXT), neuroticism (NEU), agreeableness (AGR), conscientiousness (CON), openness (OPN), and mean overall personality traits, respectively.
Volume: 37
Issue: 3
Page: 1976-1984
Publish at: 2025-03-01

Marine scientific workflow execution resource provisioner in edge-cloud platform

10.11591/ijeecs.v37.i3.pp1607-1615
Sheetal Bagali , Pradeep Kumar Siddana , Poongothai C , Kavita Avinash Patil , Vani Bengaluru Purushothama , Ashwini Kumari P
Workload application for deadline-intensive marine science, usually represented by directed acyclic graphs (DAGs), consists of interconnected operations that communicate huge quantities of information and operate on cutting-edge computational platforms. Massive communications of information among jobs running on separate computing servers, nonetheless, might come with substantial processing time, power consumption, and financial expenses. Therefore, there is room for further study of exchanging certain communications for computing to lower total interaction expenses. In addressing research this work introduces an effective resource provisioner for deadline-intensive scientific workload executer (ERP-DISWE) to reduce the makespan, consumption of energy, and cost of edge-computing platforms. The performance of ERP-DISWE is validated with the current resource provisioner using the sipht marine workload and shows superior performance concerning makespan, energy, and cost.
Volume: 37
Issue: 3
Page: 1607-1615
Publish at: 2025-03-01

Evolving strategies in anti-phishing: an in-depth analysis of detection techniques and future research directions

10.11591/ijeecs.v37.i3.pp1726-1733
Preeti Preeti , Priti Sharma
Phishing attacks are a major digital threat, impacting individuals and organizations globally. This review paper examines evolving anti-phishing strategies by analyzing five key techniques: URL blacklists, visual similarity detection, heuristic methods, machine learning models, and deep learning techniques. Each technique is evaluated for its mechanisms, unique features, and challenges. A systematic literature survey (SLR) is conducted to compare these methods; effectiveness. The paper highlights significant research challenges and suggests future directions, emphasizing the integration of artificial intelligence and behavioral analytics to combat evolving phishing tactics, this study aims to advance understanding and inspire more effective anti-phishing solutions.
Volume: 37
Issue: 3
Page: 1726-1733
Publish at: 2025-03-01

Improving unmanned armoured mobile robot navigation accuracy using Kalman filter

10.11591/ijeecs.v37.i3.pp1569-1579
Tanya Porang , Dechrit Maneetham , Padma Nyoman Crisnapati
Thailand's remarkable economic progress has earned it recognition as a development success story, but terrorism remains a significant threat, particularly in the southern provinces. To effectively combat terrorism, intelligent systems like military robots are being increasingly utilized worldwide, including the development of vision-based robots and unmanned military equipment. The Thai government aligns with this trend by aiming to manufacture military robots, including armored mobile robot (AMR) equipped with cameras for peacekeeping and surveillance purposes. The development of an automatic tank simulation robot control system integrates global position system (GPS) technology, digital compasses, and lidar sensors to enhance efficiency, directional control, and obstacle avoidance. The study investigates the implementation of Kalman filters to enhance the precision of navigation in AMRs used in military contexts. The suggested system combines GPS, Lidar sensors, and digital compasses, utilizing advanced sensor fusion algorithms to improve directional control and obstacle avoidance. The system's usefulness is demonstrated through field tests and simulations, especially in complicated contexts where conventional approaches may face difficulties. The findings help to the progress of military robotics by providing a strong answer for improving navigation accuracy in real-world situations.
Volume: 37
Issue: 3
Page: 1569-1579
Publish at: 2025-03-01

Investigations related to effect of mass density of metal oxide on specific capacitance of asymmetrical supercapacitor

10.11591/ijeecs.v37.i3.pp1435-1441
Mugdha Bhajekar , Ankur Karandikar , Sarang Joshi , Shalaka Chaphekar , P. B. Karandikar
Various types of batteries and fuel cells are widely used electrical energy storage devices in most of the applications. Supercapacitors have also become increasingly popular for energy storage in a few applications. Recently the need to develop better energy storage device has resulted in the development of asymmetrical supercapacitors (ASCs). Modeling of this device can be promising to obtain the desired characteristics to meet the need of continuous power and pulse current. For this, both positive and negative electrodes are important components for ASCs. The effect of mass density of metal oxide in positive electrode of this device is hardly researched. Component-based modeling for positive electrode is presented in the paper. Positive electrode parameters are being analyzed with the three most significant parameters affecting specific capacitance. The parameters viz. loading of materials on the current collector, percentage metal oxide, and mass density of metal oxide of the positive electrode are correlated with specific capacitance by using statistical methods to avoid the use of costly electrochemical methods. The major contribution of the presented research work is the identification of – mass density of metal oxide used in positive electrode plays the most significant role in deciding the value of the specific capacitance of this device.
Volume: 37
Issue: 3
Page: 1435-1441
Publish at: 2025-03-01
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