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All Journal Indonesian Journal of Electrical Engineering and Computer Science International Journal of Robotics and Control Systems
Islam, Amirul
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Control & Systems Engineering Electrical & Electronics Engineering

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Arduino-Controlled Multi-Function Robot with Bluetooth and nRF24L01+ Communication Ahmmed, Faysal; Rahman, Asef; Islam, Amirul; Alaly, Ajmy; Mehnaj, Samanta; Saha, Prottoy; Hossain, Tamim
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1517

Abstract

This paper outlines the design and development of an advanced robotic system that integrates hardware implementation with theoretical simulation to address the need for versatile and user-friendly robotic solutions in various environments. Addressing the issue of limited adaptability in existing robotic systems, we propose a wireless, voice and gesture-controlled robot car with an integrated robotic arm capable of performing complex tasks such as line following, obstacle avoidance, object manipulation, and autonomous navigation over one-kilometer range. To improve operational efficiency and user involvement, this paper designs a multifunctional robotic platform that integrates user-friendly control interfaces with inexpensive, state-of-the-art sensor technologies. To achieve this, we integrate a variety of sensors, including ultrasonic sensors for precise distance measurement, infrared sensors for object detection and line following, an L298 motor driver for controlling geared motors, servo motors for controlling robotic arms, a flex sensor for claw control, and an mpu6050 accelerometer for gesture recognition. The system also uses a custom-made Bluetooth app for remote control, nRF24L01+ for long-range wireless control, and Arduino Mega and Nano for processing and control functions. The results demonstrate the robot functions well in dynamic conditions, and it can be used in hospitals to assist healthcare professionals, in restaurants for food delivery, and in industrial settings for object manipulation. The system’s design proves robust in real-world scenarios, offering significant improvements in accessibility and operational efficiency. This study aligns with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 9 (Industry, Innovation, and Infrastructure), and 17 (Partnerships for the Goals). The robotic arm's potential application in healthcare settings advances SDG 3, its contribution to industrial productivity advances SDG 9, and collaborations with tech companies to expand and improve the robot's capabilities promote SDG 17.
Multi-model deep ensemble framework for early diagnosis of rare genetic disorders using genomic, Phenotypic, and EHRdata fusion Mahmood, Shafin; Akter Trina, Sayma; Saha Sukanna, Arpita; Zaman Esha, Sabrina; Amin Adib, Md. Agdam; Ahmed, Md. Sanim; Islam, Amirul
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 1: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i1.pp215-224

Abstract

Rare genetic disorders pose significant challenges in diagnosis because of their low prevalence, heterogeneous manifestations, and lack of readily available datasets. This study systematically assesses various supervised and unsuper vised machine learning methods for the early diagnosis of rare genetic disorders based on a multi-center pediatric dataset of 2,434 anonymized records enriched with demographic, clinical, and laboratory variables. In this study, genomic, phenotypic, and EHR variables were integrated into a unified feature matrix, al lowing all modalities to be jointly analyzed within each machine learning (ML) model. Following rigorous pre-processing steps, including the discard of nonin formative identifiers, imputation and encoding of categorical features, and nor malization of numerical predictors, five classification frameworks were imple mented: logistic regression (LR), random forest (RF), one-dimensional convo lutional neural network (CNN), a hybrid CNN long short-term memory (LSTM) model, and a stacked ensemble of RF and XGBoost. Model performances were evaluated on an independent test set via accuracy, precision, recall, and F1-score metrics. While LR and the CNN baseline achieved F1-scores of 0.9090 and 0.8572, respectively, tree-based models substantially outperformed deep learn ing (DL) models: RF achieved an F1-score of 0.9565, and the CNN+LSTM hybrid achieved 0.9611. RF+XGB ensemble achieved the highest diagnostic accuracy (98.77%) with balanced precision (0.9879) and recall (0.9877), illus trating its superior capacity in capturing complicated, non-linear feature interac tions and fighting against data imbalance. The results illustrate that bagging and boosting algorithms in combination provide a strong and interpretable frame work for efficient pre-screening of rare genetic disorders. The use of these ensemble techniques has the potential to enhance clinical practice by flagging high-risk cases for verification and facilitating early therapeutic intervention.
Co-Authors Ahmed, Md. Sanim Ahmmed, Faysal Akter Trina, Sayma Alaly, Ajmy Amin Adib, Md. Agdam Hossain, Tamim Mahmood, Shafin Mehnaj, Samanta Rahman, Asef Saha Sukanna, Arpita Saha, Prottoy Zaman Esha, Sabrina