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30,411 Article Results

Myoelectric grip force prediction using deep learning for hand robot

10.11591/ijai.v14.i4.pp3228-3240
Khairul Anam , Dheny Dwi Ardhiansyah , Muchamad Arif Hana Sasono , Arizal Mujibtamala Nanda Imron , Naufal Ainur Rizal , Mochamad Edoward Ramadhan , Aris Zainul Muttaqin , Claudio Castellini , Sumardi Sumardi
Artificial intelligence (AI) has been widely applied in the medical world. One such application is a hand-driven robot based on user intention prediction. The purpose of this research is to control the grip strength of a robot based on the user’s intention by predicting the grip strength of the user using deep learning and electromyographic signals. The grip strength of the target hand is obtained from a handgrip dynamometer paired with electromyographic signals as training data. We evaluated a convolutional neural network (CNN) with two different architectures. The input to CNN was the root mean square (RMS) and mean absolute value (MAV). The grip strength of the hand dynamometer was used as a reference value for a low-level controller for the robotic hand. The experimental results show that CNN succeeded in predicting hand grip strength and controlling grip strength with a root mean square error (RMSE) of 2.35 N using the RMS feature. A comparison with a state-of-the-art regression method also shows that a CNN can better predict the grip strength.
Volume: 14
Issue: 4
Page: 3228-3240
Publish at: 2025-08-01

An improved approximate parallel prefix adder for high performance computing applications: a comparative analysis

10.11591/ijict.v14i2.pp382-392
Vamsidhar Anagani , Kasi Geethanjali , Anusha Gorantla , Annamreddy Devi
Binary adders are fundamental in digital circuit designs, including digital signal processors and microprocessor data path units. Consequently, significant research has focused on improving adders’ power-delay efficiency. The carry tree adder (CTA) is alternatively referred to as the parallel prefix adder (PPA), is among the fastest adders, achieving superior performance in very large scale integrated (VLSI) implementations through efficient concurrent carry generation and propagation. This study introduces approximate PPAs (AxPPAs) by applying approximations in prefix operators (POs). Four types of AxPPAs approximate kogge-stone, approximate brent-kung, approximate ladner fischer, and approximate sparse kogge-stone-were designed and implemented on FPGA with bit widths up to 64-bit. Delay measurements from static timing analysis using Xilinx ISE design suite version 14.7 indicate that AxPPAs exhibit better latency performance than traditional PPAs. The AxPPA sparse kogge-stone, in particular, demonstrated superior area and speed performance, achieving a delay of 2.501ns for a 16-bit addition.
Volume: 14
Issue: 2
Page: 382-392
Publish at: 2025-08-01

Smart hybrid power management system in electric vehicle for efficient resource utilization with ANN

10.11591/ijict.v14i2.pp488-496
Vinoth Kumar Ponnusamy , Gunapriya Devarajan , Gomathi Easwaram , Geetha Murugesan , Rathinam Marimuthu Sekar , R. Delshi Howsalya Devi
The novel hybrid power system integrating energy storage, electric vehicle (EV) charging infrastructure and renewable energy sources improve sustainability and resilience. This work proposes a power management system controlled by artificial intelligence for EV charging infrastructure. The battery’s state of charge (SoC) is continuously monitored by artificial neural network (ANN) algorithm improves the health of the battery and efficient operation of the system. The buck boost DC-DC converter performs dynamic switching mechanism, which adjusts to changing solar conditions and energy demands, guarantees a steady power supply. Depending on the situation, the ANN algorithm used in the battery’s SoC control mechanism decides whether to priorities the EV charging or the inverter to supply the grid. The work is evaluated with the MATLAB simulation for different conditions and results are compared with different controllers such as PI, PID, and ANN. The experiment performed uses internet of things (IoT) for transferring the data from the EV motor, acts as an input for the controller to perform the novel hybrid power management operation with cloud technology. The experimental prototype evaluates the results connected to the photovoltaic (PV) system and battery management system (BMS) which lowers reliance on non-renewable resources, increases overall energy efficiency, and ensures a steady supply of power under a various condition.
Volume: 14
Issue: 2
Page: 488-496
Publish at: 2025-08-01

Creating a smart bedroom for children by connecting PIR and LDR sensors to a microcontroller Arduino UNO ATmega328P

10.11591/ijict.v14i2.pp540-554
Ragmi M. Mustafa , Kujtim R. Mustafa , Refik Ramadani
Intelligent electronic systems are increasingly prevalent in modern society. The development of smart bedrooms for young children, especially those with developmental disabilities, it is based on the responses of passive infrared (PIR) and light dependent resistor (LDR) sensors. The PIR sensor detects children’s movement during the night, triggering the microcontroller to send a bit of 1 to the microcontroller pin connected to an electromagnetic relay, which then switches on a 220 VAC light to illuminate the bedroom. This only occurs if the LDR sensor has high resistance, indicating that the environment is completely dark. The functionality of this intelligent system mainly depends on the program code (sketch) uploaded to the Arduino UNO microcontroller module. The microcontroller is programmed to perform specific functions based on the sensors data. It is based on the responses of PIR and LDR sensors. The PIR sensor detects children’s movement during the night, triggering the microcontroller to send a bit of 1 to the microcontroller pin connected to an electromagnetic relay, which then switches on a 220 VAC light to illuminate the bedroom. This only occurs if the LDR sensor has high resistance, indicating that the environment is completely dark.
Volume: 14
Issue: 2
Page: 540-554
Publish at: 2025-08-01

Performance analysis of LDPC codes in MIMO-OFDM for next generation wireless systems

10.11591/ijict.v14i2.pp636-644
P. Aruna Kumari , Srinu Pyla , U. N. V. P. Rajendranath , Nirujogi Venkata Maheswara Rao
Fifth Generation communication systems overcome the limitations of the fourth-generation systems and ensure improved data rates, lower latency, and higher connection density. 5G technology has the potential to unlock new internet of things (IoT) applications by utilizing the technologies such as multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM), and Li-Fi. Low density parity check (LDPC) and polar codes are being preferred for data and control channels respectively in 5G systems as these coding techniques offer good error-detection and correction along with reduced latency. Morever, LDPC codes are power efficient. This paper aims to analyze the bit error rate (BER) performance of LDPC codes in MIMO-OFDM System for different modulation schemes. LDPC codes improve the BER performance of OFDM and MIMO-OFDM systems. MIMO-OFDM systems deliver better BER performance over OFDM system.
Volume: 14
Issue: 2
Page: 636-644
Publish at: 2025-08-01

Driving agricultural evolution: implementing agriculture 4.0 with Raspberry Pi and internet of things in Morocco

10.11591/ijai.v14.i4.pp3462-3473
Raja Mouachi , Elbelghiti Youssef , Sanaa El mrini , Mustapha Ezzini , Mustapha Raoufi
The purpose of this project was to investigate the use of embedded system and smartphone technologies in conjunction with Raspberry Pi and NodeMCU to create an intelligent system for smart farming (SF). By means of experiments and comparative analysis carried out in several agricultural contexts, the research evaluated the efficacy of the intelligent system. Results showed that the system was able to handle pertinent agricultural activities and effectively monitor important environmental factors including temperature, humidity, soil moisture, and climatic quality. The system's remote accessibility helped farmers by allowing them to effectively oversee agricultural operations at any time and from any location. As a consequence, SF techniques produced more production, lower costs, and maintained assets.
Volume: 14
Issue: 4
Page: 3462-3473
Publish at: 2025-08-01

Deep learning algorithms for breast cancer detection from ultrasound scans

10.11591/ijict.v14i2.pp427-437
Lawysen Lawysen , Gede Putra Kusuma
Breast cancer is a highly dangerous disease and the leading cause of cancer related deaths among women. Early detection of breast cancer is considered quite challenging but can offer significant benefits, as various treatment interventions can be initiated earlier. The focus of this research is to develop a model to detect breast cancer based on ultrasound results using deep learning algorithms. In the initial stages, several preprocessing processes, including image transformation and image augmentation were performed. Two types of models were developed: utilizing mask files and without using mask files. Two types of models were developed using four deep learning algorithms: residual network (ResNet)-50, VGG16, vision transformer (ViT), and data-efficient image transformer (DeiT). Various algorithms, such as optimization algorithms, loss functions, and hyperparameter tuning algorithms, were employed during the model training process. Accuracy used as the performance metric to measure the model’s effectiveness. The model developed with ResNet-50 became the best model, achieving an accuracy of 94% for the model using mask files. In comparison, the model developed with ResNet-50 and DeiT became the best model for the model without mask files, with an accuracy of 80%. Therefore, it can be concluded that using mask files is crucial for producing the best-performing model.
Volume: 14
Issue: 2
Page: 427-437
Publish at: 2025-08-01

Advanced predictive models for thyroid disease comorbidities using machine learning and deep learning: a comprehensive review

10.11591/ijict.v14i2.pp673-683
Mohammed Yacoob B. A. , Jayashree J.
With advances in machine learning (ML) and deep learning (DL), the future of thyroid disease diagnosis and prognosis looks very bright. The integration of various data such as imaging and medical record data has increased the accuracy of the model. Advanced DL models such as convolutional neural network (CNN) and recurrent neural network (RNN) further improved disease detection in precision medicine. However, some of the major disadvantages of effective clinical integration include unbalanced samples, unclear sampling, having to communicate in different populations, decreased physician confidence due to the vagueness of current models therefore, and few studies available to identify thyroid comorbidities such as polycystic ovary syndrome (PCOS) and thyroid eye disease (TED) in a variety of different populations to develop the line. It is important to focus future research activities on model definition and validation an improving and thus the diagnosis and prognosis of thyroid comorbidities is of utmost importance. What this will bring is ML and DL, an opportunity to make very significant improvements in the diagnosis, treatment, and management of thyroid diseases, thereby improving patient outcomes and health care by seeking crystals as a group they work interdisciplinary to collaborate in developing flexible solutions, sharing knowledge, and responding to these stated deficiencies.
Volume: 14
Issue: 2
Page: 673-683
Publish at: 2025-08-01

Generative Indonesian chatbot for university major selection using transformers embedding

10.11591/ijai.v14.i4.pp3474-3482
Mutiara Auliya Khadija , Bambang Harjito , Morteza Saberi , Astrid Noviana Paradhita , Wahyu Nurharjadmo
Selecting a university major is a crucial decision that impacts students' future career paths and personal fulfillment. Traditional guidance methods often lack the personalization and timeliness needed to support students effectively. This study explores the use of Indonesian generative artificial intelligence (AI) chatbots and transformer embeddings to enhance decision-making for university major selection. By leveraging advanced AI techniques, such as bidirectional encoder representations from transformers (BERT) and Gemini embeddings, the research aims to provide personalized, interactive, and contextually relevant guidance. Experiments showed that BERT embeddings achieved the highest accuracy, with recurrent neural network (RNN) and long short-term memory (LSTM) models also performing well but facing issues with overfitting. Gemini embeddings provided strong performance but slightly less effective than BERT. The findings suggest that BERT-based models with RNN are superior for developing decision-support systems in 92% accuracy. Future work should focus on further optimization and integration of user feedback to ensure the relevance and effectiveness of these AI tools in educational settings.
Volume: 14
Issue: 4
Page: 3474-3482
Publish at: 2025-08-01

Benefits and challenges of graduate start-up and academic spin-off model integration: a systematic review

10.11591/ijere.v14i4.30288
Fakhrul Anwar Zainol , Wan Norhayate Wan Daud , Syamsul Azri Abdul Rahman , Safrul Izani Mohd Salleh , Balogun Daud Ishola
Government representatives and university administrators must comprehend the reasons behind academics’ desire to start their own businesses to create laws that effectively encourage academics to take up entrepreneurship. One may understand how seemingly difficult it might be to foster creativity and entrepreneurship in a varied community, considering how difficult it can be to teach entrepreneurship to university students. Consequently, the goal of this systematic review was to summarize the challenges and benefits of integration of graduate start-up and academic-spin off model. Three internet databases were searched for articles between 2010 and 2023 (i.e., a cumulative index using Scopus, the Web of Science, and Emerald to provide a summary of the challenges and benefits of graduate start-up and academic spin-off models). The study adds to a thorough understanding of the complex nature of business models by highlighting the models’ dynamic evolution over time, the value of global collaboration, the necessity of carefully examining individual models, and the strategic diversity that comes from exploring several business models simultaneously. When taken as a whole, these observations offer insightful information that decision-makers, business owners, and academics may use to better understand, traverse, and navigate the terrain of innovation and entrepreneurial processes.
Volume: 14
Issue: 4
Page: 2945-2955
Publish at: 2025-08-01

Design and implementation of VR lato-lato STEAM in engineering education science

10.11591/ijere.v14i4.32206
Septian Rahman Hakim , Didik Hariyanto , Nadi Suprapto , Yulkifli Yulkifli , Hafizhuddin Zul Fahmi , Khoirun Nisa , Iqbal Ainur Rizki
This research focuses on integrating science, technology, engineering, art, and mathematics (STEAM) and virtual reality (VR) in engineering-science education through a lato-lato game (clackers ball). The research analyzes the development trends of VR, particularly the integration of the lato-lato game in STEAM education. Using a waterfall model, the research develops and tests VR-STEAM applications, involving analysis, design, development, and testing phases, with expert validation and student implementation. Data was collected from 60 Indonesian university students and analyzed using partial least squares structural equation modeling (PLS-SEM). Over the past 5 years, VR in education has surged, enhancing learning outcomes and student engagement. The study shows that integrating technology and pedagogy is crucial for effective STEAM education. The design of VR-STEAM is validated and ready for use. PLS-SEM analysis indicates that the indicators are valid and reliable for measuring university students’ experiences. The study reveals that integrating STEAM with VR significantly enhances engineering-science learning (ESL), focusing on understanding, personal goals, communication, and cooperation. VR serves as a mediator that fosters 21st-century skills. The research recommends developing assessment tools to measure VR-STEAM ethnoscience and evaluate effectiveness, highlighting the importance of technology in improving learning and connecting concepts to real-world phenomena like lato-lato.
Volume: 14
Issue: 4
Page: 2749-2761
Publish at: 2025-08-01

Analysis of bibliometrics in studying the influence of the environment on preschool children’s psychological development

10.11591/ijere.v14i4.30817
Nguyen Thi Ut Sau , Tran Thi Nhung , Pham Thi Kieu Oanh , Vu Thi Thuy , Le Thi Thuong Thuong , Nguyen Thi Hoa
This article aims to provide an overall picture of research on the environment’s influence on preschool children’s psychological development. The researchers used the preferred reporting items for systematic reviews and meta-analysis (PRISMA) method to collect data and VOSviewer software to analyze 119 articles from the Scopus database from 2000 to 2023. The results showed that since 2006, i) the environmental influence on the psychological development of preschool children has received much attention; ii) the United States and the United Kingdom are the two leading countries in terms of the number of publications; iii) Leve, Neiderhiser, Reiss, and Shaw are the four leading authors; iv) 16 out of 20 influential journals in this field are Q1 journals, most of which belong to educational psychology. The two main concerns of the authors in these 119 articles are “parenting” and “development.” In the past five years, researchers have focused on topics such as “autism,” “preschoolers,” “environment,” “COVID-19”, and “externalizing problems”.
Volume: 14
Issue: 4
Page: 3051-3064
Publish at: 2025-08-01

Gender differences in motivation and problem-solving in a physics course online problem-based learning

10.11591/ijere.v14i4.31105
Elnetthra Folly Eldy , Fauziah Sulaiman , Mohd Zaki Ishak , Lorna Uden , Jo-Ann Netto-Shek
Online learning has been crucial since COVID-19, yet its effectiveness, particularly in physics education, remains debated. Understanding students’ motivation and problem-solving abilities in online environments is critical. This paper examined and presented the gender difference in motivation and problem-solving skills using an integrated online problem-based learning (iON-PBL) in a physics course. Developed using analysis, design, development, implementation, and evaluation (ADDIE) mode, iON-PBL module of physics guided students through problem-solving activities over 13 weeks. A post-test–delayed post-test design was used to assess retention of motivation and problem-solving skills. The study involved 116 pre-university students from Universiti Malaysia Sabah (88 females, 28 males). Motivation was measured using the motivated strategies for learning questionnaire (MSLQ) (four components), and problem-solving skills were assessed with the problem-solving inventory (PSI) (three components). Data analysis was conducted using SPSS version 28. Findings showed a significant gender difference in the ‘cognitive strategy’ component of motivation at the post-test, favoring female students. However, this difference was not sustained in the delayed post-test. In contrast, no gender difference was found in problem-solving at the post-test, but females scored significantly higher in ‘personal control’ in the delayed post-test. These findings suggest that female students are more likely to maintain cognitive strategies and personal control in online learning. Educators should consider targeted strategies to support male students’ motivation and problem-solving development in virtual environments to foster gender equity. Educators should consider targeted strategies to support male students’ motivation and problem-solving development in virtual environments to foster gender equity.
Volume: 14
Issue: 4
Page: 2832-2845
Publish at: 2025-08-01

Enhancing logo security: VGG19, autoencoder, and sequential fusion for fake logo detection

10.11591/ijict.v14i2.pp506-515
Debani Prasad Mishra , Prajna Jeet Ojha , Arul Kumar Dash , Sai Kanha Sethy , Sandip Ranjan Behera , Surender Reddy Salkuti
This paper deals with a way of detecting fake logos through the integration of visual geometry group-19 (VGG19), an autoencoder, and a sequential model. The approach consists of applying the method to a variety of datasets that have gone through resizing and augmentation, using VGG19 for extracting features effectively and autoencoder for abstracting them in a subtle manner. The combination of these elements in a sequential model account for the improved performance levels as far as accuracy, precision, recall, and F1-score are concerned when compared to existing approaches. This article assesses the strengths and limitations of the method and its adapted comprehension of brand identity symbols. Comparative analysis of these competing approaches reveals the benefits resulting from such fusion. To sum up, this paper is not only a major contribution to the domain of counterfeit logo detection but also suggests prospects for enhancing brand security in the digital world.
Volume: 14
Issue: 2
Page: 506-515
Publish at: 2025-08-01

Influence of playing online video games on Filipino college students’ confidence in speaking English

10.11591/ijere.v14i4.32842
Allan Jay Esteban , Kiwan Sung
Online video games that require players to communicate in English provide opportunities for students to practice their language skills and overcome their fear of speaking in English. Unfortunately, the literature reveals an existing gap in investigating how such games can influence students’ confidence in speaking English, especially in the Philippine context. Therefore, this study surveyed 148 Filipino college English-as-a-second language (ESL) students to examine differences in their perceived confidence in speaking English depending on learner variables such as gender, time spent online gaming (TSOG), number of games played (NOGP), self-rated speaking proficiency (SRSP), and game interactivity.Using independent t-tests and one-way analysis of variance (ANOVA) analyses, results revealed statistically significant differences in the development of communication skills in English (DCSE) depending on the TSOG, willingness to communicate (WTC) in English depending on the NOGP, and enhancement of communication skills in English, active participation in class, and reduced anxiety in using English (RAUE) depending on the SRSP. This exploratory study indicates that online video games can be valuable tools in increasing English speaking confidence among Filipino college students. Further research is posited to understand the extent to which online games influence ESL learners’ speaking confidence in different educational and cultural contexts.
Volume: 14
Issue: 4
Page: 2555-2564
Publish at: 2025-08-01
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