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24,371 Article Results

Video conferencing algorithms for enhanced access to mental healthcare services in cloud-powered telepsychiatry

10.11591/ijece.v15i1.pp1142-1151
Rajagopalan Senkamalavalli , Subramaniyan Nesamony Sheela Evangelin Prasad , Mahalingam Shobana , Chellaiyan Bharathi Sri , Rajendar Sandiri , Jayavarapu Karthik , Subbiah Murugan
Exploring the video conferencing algorithms for cloud-powered telepsychiatry to improve mental healthcare access. The goal is to evaluate and optimise these algorithms' latency, bandwidth utilisation, packet loss, and jitter across worldwide locations. To provide a smooth and high-quality virtual consultation between patients and mental health providers. Using performance data to identify areas for development, the effort aims to lower technological hurdles and increase telepsychiatry session dependability. Findings will help create strong, efficient algorithms that can handle different network situations, increasing patient outcomes and extending mental healthcare services. In the 1st instance latent analysis in a sample of 5 cities, the average latency (ms) is 45, the peak latency is 120, the off-peak latency is 30, and the packet loss is 0.5. In another instance, bandwidth utilisation in a sample of 5 sessions ranged from 30 to 120 minutes, with data supplied in MB - 150-600 and received in MB - 160-620, with average bandwidth (Mbps) - 5-15 and maximum bandwidth: 10-20.
Volume: 15
Issue: 1
Page: 1142-1151
Publish at: 2025-02-01

Predictive modeling for healthcare worker well-being with cloud computing and machine learning for stress management

10.11591/ijece.v15i1.pp1218-1228
Muthukathan Rajendran Sudha , Gnanamuthu Bai Hema Malini , Rangasamy Sankar , Murugaaboopathy Mythily , Piskala Sathiyamurthy Kumaresh , Mageshkumar Naarayanasamy Varadarajan , Shanmugam Sujatha
This paper provides a new method for stress management-focused predictive modeling of healthcare workers' well-being via cloud computing and machine learning. The need for proactive measures to track and assist healthcare workers' mental health is highlighted by the rising expectations placed on them. Using various data sources, our system compiles information from surveys, social media, electronic health records, and wearable devices into a single location for analysis. Predictive models that predict healthcare workers' stress levels and well-being are developed using gradient boosting, a strong machine learning (ML) technique. This work is suitable for gradient boosting due to its resilience to overfitting and capacity to process many kinds of data. Healthcare organizations may improve the health of their employees by using our technology to detect stress patterns and identify the causes of that stress. It can use specific treatments and support systems to alleviate that stress. Widespread adoption and real-time monitoring are made possible by the scalability, flexibility, and accessibility of cloud computing infrastructure. This method shows promise in the direction of proactive solutions driven by data for controlling the stress of healthcare workers and improving their general well-being.
Volume: 15
Issue: 1
Page: 1218-1228
Publish at: 2025-02-01

Product reviews analysis to extract sentimental insights with class confidence rate using self-organizing map neural network

10.11591/ijece.v15i1.pp980-994
Sara Ahsain , Yasyn Elyusufi , M'hamed Ait Kbir
Customer data analysis helps companies to understand customer intentions and behaviors better. This study introduces an analysis of product reviews to help managers adopt a more efficient strategy to extract valuable knowledge and help detect segment of customers that need a special attention and products that need improvement or with the most impact. The used dataset is a set of Amazon reviews divided into multiple categories; each review has a target column called ‘overall’ that takes a value between 1 and 5 (customer's satisfaction). Based on the ‘overall’ column, multiple labeling methods have been used and compared to get a binary target variable, positive or negative, that affects a class to a review. This dataset contains more than one million reviews and can give companies great insight into products’ quality and customers’ retention. This work has materialized by using customer segmentation and competitive learning with self-organizing map (SOM) Model and adopting a new approach to explore the generated network/map, it is based on clustering and map nodes labelling using a majority voting process. The results show that the proposed dual approach combining the prior knowledge, related to supervised learning, and the competitive learning abilities enhances the SOM model’s capabilities.
Volume: 15
Issue: 1
Page: 980-994
Publish at: 2025-02-01

Enhancing the reliance of emergency power supply systems for nuclear facilities using hybrid system

10.11591/ijece.v15i1.pp36-45
Mohammed Saade , Hussein El-Eissawi , Adel S. Nada
The performance of an atomic facility depends on the efficient supply of electricity, particularly emergency loads like monitoring and control equipment, radiation safety systems, and emergency lights. Most nuclear facilities rely on diesel generators to supply emergency loads during grid outages. Due to the diesel generator's imperfections, such as its starting time, it may fail to deliver power because it is unavailable due to maintenance, failure to start, or failure to run and supply the load. It cannot immediately supply the critical loads, resulting in a blackout and the release of radioactive substances into the environment. To address the previous issues, this paper proposes an improved method to enhance the reliability of nuclear facilities for providing electricity to safety and critical consumers during normal and emergency operating modes. The approach incorporates a photovoltaic (PV) system/battery, and its robustness and performance are tested using load flow and transient stability analysis. The simulation results demonstrated the effectiveness and speed of the proposed method when compared to the traditional method, as the emergency consumers were successfully powered within a very short time without fluctuations, and the voltage reduction and frequency were within the nominal values. The electrical transient analyzer program (ETAP) is used to validate these results.
Volume: 15
Issue: 1
Page: 36-45
Publish at: 2025-02-01

Robust adaptive integral sliding mode control of a half-bridge bidirectional DC-DC converter

10.11591/ijece.v15i1.pp114-128
Julius Derghe Cham , Francis Lénine Djanna Koffi , Alexandre Teplaira Boum , Ambe Harrison
A novel approach to improving the dynamic response of a half-bridge bidirectional DC-DC converter is presented in this paper, particularly in the face of disturbances from internal or external sources. These converters, which are integral to the operation of DC microgrids, are responsible for stepping up or stepping down voltage as required. To optimize the converter's performance under varying conditions, we propose an adaptive integral sliding mode controller (AISMC) enhanced by particle swarm optimization (PSO). The proposed controller leverages the strengths of both super-twisting sliding mode control (STSMC) and adaptive control, providing a robust and responsive solution to the challenges posed by the converter's nonlinear dynamics. The system's stability is rigorously ensured through the application of Lyapunov stability criteria, which underpin the enhanced performance of the controller. Simulations conducted in the MATLAB/Simulink environment demonstrate that the AISMC-PSO outperforms conventional control strategies, offering superior stability, robustness, and precision. The results clearly indicate that the proposed approach minimizes errors and enhances the overall efficiency and reliability of the bidirectional half-bridge DC-DC converter, making it a highly effective solution for DC microgrid applications.
Volume: 15
Issue: 1
Page: 114-128
Publish at: 2025-02-01

Handling class imbalance in education using data-level and deep learning methods

10.11591/ijece.v15i1.pp741-754
Rithesh Kannan , Hu Ng , Timothy Tzen Vun Yap , Lai Kuan Wong , Fang Fang Chua , Vik Tor Goh , Yee Lien Lee , Hwee Ling Wong
In the current field of education, universities must be highly competitive to thrive and grow. Education data mining has helped universities in bringing in new students and retaining old ones. However, there is a major issue in this task, which is the class imbalance between the successful students and at-risk students that causes inaccurate predictions. To address this issue, 12 methods from data-level sampling techniques and 2 methods from deep learning synthesizers were compared against each other and an ideal class balancing method for the dataset was identified. The evaluation was done using the light gradient boosting machine ensemble model, and the metrics included receiver operating characteristic curve, precision, recall and F1 score. The two best methods were Tomek links and neighbourhood cleaning rule from undersampling technique with a F1 score of 0.72 and 0.71 respectively. The results of this paper identified the best class balancing method between the two approaches and identified the limitations of the deep learning approach.
Volume: 15
Issue: 1
Page: 741-754
Publish at: 2025-02-01

Digital adaptive control with pulse width modulation of signals

10.11591/ijece.v15i1.pp252-259
Isamiddin Siddikov , Gulchekhra Alimova , Malika Rustamova , Mustafaqul Usanov
The paper presented research results of a digital control system for a dynamic plant with pulse-width modulation (PWM) of control impacts. As the control PWM signal is taken the pulse duty cycle, is calculated on each current cycle of the sample from the measured values. A control algorithm is proposed based on a hybrid application of the linear-quadratic optimization procedure and the theory of observers of minimal complexity. To ensure execution that the conditions of Astatism are met, the dynamic model of the plant is supplemented with a discrete integrator. The proposed approach makes it possible to reduce hardware costs and increase the robustness of the control system due to the exclusion of operations for digital–analogue transformations of signals. The proposed algorithm for digital control of a dynamic plant with varying duty cycle values of the PWM signal shows that the PWM model turned out to be linear and practically inertia less, which makes it easy to take into account the modulator model, which significantly simplifies the solution of the problem of synthesizing a control system for a dynamic plant. The possibility of receiving a high-quality modulated control signal allows for significant suppression of signal pulsations and high control accuracy.
Volume: 15
Issue: 1
Page: 252-259
Publish at: 2025-02-01

Field programmable gate array implementation frameworks of a variable-length pseudorandom pattern generator

10.11591/ijece.v15i1.pp186-195
Geethu Remadevi Somanathan , Ramesh Bhakthavatchalu
A pseudorandom pattern generator produces sequences similar to true random sequences. A variable length pseudo-random pattern generator (PRPG), which can be used as a pattern generator for various applications like built-in self-test (BIST) or cryptography, is proposed in this paper. Our work is based on a linear feedback shift register circuit platform. The proposed design can generate patterns corresponding to different characteristic polynomials of any given polynomial degree. These characteristic polynomials are integrated into the linear feedback shift register circuit by providing an option to select the feedback paths of any of these polynomials. This paper implements and evaluates the proposed design for primitive and non-primitive characteristic polynomials of degrees 3 to 15. The circuit generates output patterns of different periods based on user inputs. Compared to other pseudorandom pattern generator circuits, the proposed circuit can generate a large set of patterns and consumes less power. Adequate results from the experiments demonstrate the functionalities and performance of the proposed pattern generator from degrees 3 to 15. The proposed circuit generates pseudorandom patterns that can be used not only for built-in self-test but also for cryptography and wireless communication applications.
Volume: 15
Issue: 1
Page: 186-195
Publish at: 2025-02-01

Measuring anxiety level on phobia using electrodermal activity, electrocardiogram and respiratory signals

10.11591/ijece.v15i1.pp337-348
Khusnul Ain , Osmalina Nur Rahma , Endah Purwanti , Richa Varyan , Sayyidul Istighfar Ittaqilah , Danny Sanjaya Arfensia , Tiara Dyah Sosialita , Fitriyatul Qulub , Rifai Chai
People with spider phobia experience excessive anxiety reactions when exposed to spiders that will interfere with daily life. Diagnosing and measuring anxiety levels in patients with spider phobia is a complex challenge. Conventional diagnosis requires psychological evaluations and clinical interviews that take time and often result in a high degree of subjectivity. Therefore, there is a need for a more objective and efficient approach to measuring anxiety levels in patients. This study performs anxiety level classification based on electrodermal activity, electrocardiogram (ECG) and respiratory signals using the dataset of Arachnophobia subjects. Each raw data is preprocessed using 24 types of features. Feature performance is processed using the recursive feature elimination method. Data processing was performed in 3 anxiety levels (high, medium, low) and two anxiety levels (high, low) with the support vector machine method and hold-out validation method (7:3). The performance of the model is evaluated by showing the accuracy, precision, recall and F1 score values. The polynomial kernel can perform optimal classification and obtain 100% accuracy in 2 classes and three classes with 100% precision, recall, and F1 score values. This result shows excellent potential in measuring anxiety levels that correlate with mental health issues.
Volume: 15
Issue: 1
Page: 337-348
Publish at: 2025-02-01

ReRNet: recursive neural network for enhanced image correction in print-cam watermarking

10.11591/ijece.v15i1.pp356-364
Said Boujerfaoui , Hassan Douzi , Rachid Harba , Khadija Gourrame
Robust image watermarking that can resist camera shooting has gained considerable attention in recent years due to the need to protect sensitive printed information from being captured and reproduced without authorization. Indeed, the evolution of smartphones has made identity watermarking a feasible and convenient process. However, this process also introduces challenges like perspective distortions, which can significantly impair the effectiveness of watermark detection on freehandedly digitized images. To meet this challenge, ResNet50-based ensemble of randomized neural networks (ReRNet), a recursive convolutional neural network-based correction method, is presented for the print-cam process, specifically applied to identity images. Therefore, this paper proposes an improved Fourier watermarking method based on ReRNet to rectify perspective distortions. Experimental results validate the robustness of the enhanced scheme and demonstrate its superiority over existing methods, especially in handling perspective distortions encountered in the print-cam process.
Volume: 15
Issue: 1
Page: 356-364
Publish at: 2025-02-01

Sailfish-cat algorithm-enhanced generative adversarial network for attack detection in internet of things-Fog network authentication

10.11591/ijece.v15i1.pp1109-1122
Pallavi Kanthamangala Niranjan , Ravikumar Venkatesh
The internet of things (IoT) has emerged as a prominent and influential concept within the realm of computing. Various attack detection methods are devised for detecting attacks in IoT-Fog environment. Despite all these efforts, attack detection still remained as a challenging task due to factors such as low latency, resource constraints of IoT devices, scalability issues, and distribution complexities. All these challenges are addressed in this paper by designing an efficient attack detection technique named as sailfish- cat optimization-based generative adversarial network (SaCO-based GAN) tailored for the IoT-Fog framework. This proposed approach introduces the SaCO-based GAN for IoT-Fog attack detection utilizing deep learning and feature-based classification, validated through experiments showing superior performance metrics. Notably, the SaCO optimization technique is utilized to train the GAN. Experimental results demonstrate the efficacy of the SaCO-based GAN with a maximum recall of 92.15%, a maximum precision of 91.21%, and a maximum F-Measure of 92.16%, outperforming existing techniques in IoT-Fog attack detection. The paper recommends enhancing scalability, implementing real-time detection strategies, rigorously testing robustness against diverse attack scenarios, and integrating with existing IoT security frameworks for practical deployment.
Volume: 15
Issue: 1
Page: 1109-1122
Publish at: 2025-02-01

Timed concurrent system modeling and verification of home care plan

10.11591/ijece.v15i1.pp870-882
Acep Taryana , Dieky Adzkiya , Muhammad Syifa'ul Mufid , Imam Mukhlash
A home care plan (HCP) can be integrated with an electronic medical records (EMR) system, serving as an example of a real-time system with concurrent processes. To ensure effective operation, HCPs must be free of software bugs. In this paper, we explore the modeling and verification of HCPs from the perspective of scheduling data operationalization. Specifically, we investigate how patients can obtain home services while preventing scheduling conflicts in the context of limited resources. Our goal is to develop and verify robust models for this purpose. We employ formalism to construct and validate the model, following these steps: i) develop requirements and specifications; ii) create a model with concurrent processes using timed automata; and iii) verify the model using UPPAAL tools. Our study focuses on HCP implementation at a regional general hospital in Banyumas District, Central Java, Indonesia. The results include models and specifications based on timed automata and timed computation tree logic (TCTL). We successfully verified a concurrent model that utilizes synchronized counter variables and a sender-receiver approach to analyze collision constraints arising from the synchronization of patient and resource plans.
Volume: 15
Issue: 1
Page: 870-882
Publish at: 2025-02-01

Optimal cleaning robot on solar panels with time-sequence input based on internet of things

10.11591/ijece.v15i1.pp280-291
Dwi Nur Fitriyanah , Rivaldi Dwi Pramana Saputra , Imam Abadi , Ali Musyafa
Solar panels are the main component of solar power generation systems, and they function by converting solar energy into electrical energy. Indonesia has great potential for solar energy. Solar panels will work optimally at temperatures of 25 °C to 28 °C. The greater the temperature of the solar panel, the more power generated by the panel. The influence of solar radiation intensity can be caused by dust and animal droppings attached to the surface of the solar panel module. If the surface of a solar panel is covered with dust or dirt, which can block the entry of solar radiation, the resulting power output is not optimal. The aim of this research is to design and implement an automatic cleaning system for solar power plants. The system used is using ESP32 based on the Blynk application and adding internet of things (IoT) devices with a cleaning method using pumped water spraying, then assisted with wipers which have silicon rubber material to clean dust and dirt. Based on the cleaning optimization simulation calculations, we found that the optimal or efficient cleaning condition was once a month, with an efficiency of 75.17%.
Volume: 15
Issue: 1
Page: 280-291
Publish at: 2025-02-01

Energy analysis of active photovoltaic cooling system using water flow

10.11591/ijece.v15i1.pp1-14
Ant. Ardath Kristi , Erwin Susanto , Agus Risdiyanto , Agus Junaedi , Rudi Darussalam , Noviadi Arief Rachman , Ahmad Fudholi
An active water-cooling system is one of several technologies that has been proven to be able to reduce heat losses and increase electrical energy in photovoltaic (PV) module. This research discusses a comparative experimental study of three pump activation controls in cooling of PV module with the aim of evaluating specifically the PV output power, net energy gain, water flow rate, and module temperature reduction. The three pump activation controls being compared are continuously active during the test, active based on setpoint temperature, and active by controlling the pump voltage using pulse width modulation (PWM) control in adjusting water flow rate smoothly. The results show that controlling the pump voltage using PWM in the PV cooling process produces energy of 437.95 Wh, slightly lower than the others and the average module cooling temperature is 35.24 °C, higher of 1-3 °C than the others. Nevertheless, PWM control of cooling pump has resulted the percentage of net energy gain of 9.94%, greater than other controls, and with an average flow rate of 2.17 L/min, more efficient than the others. Thus, this control is quite effective as it can produce higher net PV energy yield and lower water consumption.
Volume: 15
Issue: 1
Page: 1-14
Publish at: 2025-02-01

Method of undetermined coefficients for circuits and filters using Legendre functions

10.11591/ijece.v15i1.pp846-854
Zhanat Manbetova , Pavel Dunayev , Assel Yerzhan , Manat Imankul , Zhazira Zhazykbayeva , Zhadra Seitova , Raushan Dzhanuzakova , Gayni Karnakova
This article presents a new way to implement matching networks and filters using the method of undetermined coefficients. A method is proposed for approximating the transmission coefficient of the synthesized filter, taking into account the required amplitude-frequency characteristics. To synthesize the filter, an approximating function (AF) was used using orthogonal Legendre polynomials, which is a mathematical description using a system of equations. Filter properties whose implementation is based on modified Legendre approximating functions usually depend on the interval on which they are defined and have the property that they are orthogonal on this interval. An example of seventh order filter synthesis using modified Legendre approximating functions is given. The filter circuit is implemented, the elements of the filter circuit are calculated based on the selected approximating modified function. The criteria used were minimization of the unevenness of the group delay time (GDT) and minimization of the complex approximation error for given values of the AF parameters. As a result, the number of filter elements, the group delay value and the complex approximation error are significantly reduced.
Volume: 15
Issue: 1
Page: 846-854
Publish at: 2025-02-01
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