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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) International Journal of Reconfigurable and Embedded Systems (IJRES) International Journal of Informatics and Communication Technology (IJ-ICT) International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics
Uddin, Jia
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Automotive Engineering Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory Ferdoush, Zannatul; Mahmud, Booshra Nazifa; Chakrabarty, Amitabha; Uddin, Jia
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i1.pp763-771

Abstract

In the presence of the deregulated electric industry, load forecasting is more demanded than ever to ensure the execution of applications such as energy generation, pricing decisions, resource procurement, and infrastructure development. This paper presents a hybrid machine learning model for short-term load forecasting (STLF) by applying random forest and bidirectional long short-term memory to acquire the benefits of both methods. In the experimental evaluation, we used a Bangladeshi electricity consumption dataset of 36 months. The paper provides a comparative study between the proposed hybrid model and state-of-art models using performance metrics, loss analysis, and prediction plotting. Empirical results demonstrate that the hybrid model shows better performance than the standard long short-term memory and the bidirectional long short-term memory models by exhibiting more accurate forecast results.
A holistic approach of stability using material parameters of manipulators Mustary, Shabnom; Kashem, Mohammod Abul; Chowdhury, Mohammad Asaduzzaman; Uddin, Jia
IAES International Journal of Robotics and Automation (IJRA) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v13i4.pp380-390

Abstract

The demand for a comprehensive method to assess stability using manipulator material parameters is high. Various material parameters, such as the Young modulus, which represents stiffness, damping, and deflection, influence the material of the robot manipulator. The correlation between robot stability and these characteristics remains unclear, as prior studies have not yet examined the collective impact of these parameters on robot manipulators. This work considers two sophisticated manipulators, namely ABB and FANUC. The main objective of this research is to construct a stability model that considers the material properties of stiffness, damping, and deflection to assess the manipulator’s stability level, which may be categorized as low, medium, or high. Furthermore, the presented stability model examines and employs numerous modified and conventional formulas for material properties to determine the level of stability. The findings show that stiffness significantly influences the stability of robot manipulators, a relationship that applies to all the examined manipulators. We also emphasize that the choice of manipulator materials significantly impacts stability maintenance. These findings are expected to enhance the design and advancement of novel robot manipulators within the industry.
Algorithm-driven development of a simulation tool for industrial manipulator stability analysis Mustary, Shabnom; Kashem, Mohammod Abul; Chowdhury, Mohammad Asaduzzaman; Uddin, Jia
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i1.pp69-78

Abstract

Industrial manipulators are essential to many manufacturing processes because they increase efficiency and productivity dramatically. However, maintaining operational safety and averting potential risks in industrial environments requires that these manipulators be stable. The development and implementation of an entirely algorithm-driven novel simulation tool intended to assess industrial manipulators’ stability in-depth are presented in this research. The suggested tool combines sophisticated mathematical models with the material properties of the manipulator, such as deflection, stiffness, and damping. To analyses the dynamic behaviour of manipulators under various operating situations, a hypothetical simulation technique to assess the stability of robot manipulators combined with material properties is taken into consideration. The simulation tool offers vital insights into the stability characteristics of manipulators, allowing engineers and designers to enhance their performance and guarantee operational safety. The simulation tool’s usefulness is showcased through case studies and comparative evaluations, emphasizing its capacity to improve the design and implementation of industrial manipulators in practical situations. In summary, this research enhances the field of industrial automation by offering a strong framework for assessing and upgrading the stability of manipulator systems. This, in turn, improves productivity and safety in industrial settings.
Cost-effective IoT-based automated vehicle headlight control system: design and implementation Begum, Momotaz; Ullah, Nayeem; Shuvo, Mehedi Hasan; Islam, Towhidul; Hossen, Thofazzol; Uddin, Jia
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp325-333

Abstract

The current world would be difficult without vehicles, which offer vital advantages for social connectivity, mobility, and technical advancement. Though motor vehicles provide benefits to passenger transportation, they also present certain challenges in their use. A major issue is nighttime traffic accidents caused by headlamps from automobiles traveling in reverse directions, that's why there is a high probability of accidents due to the glare on the driver's eyes. The phrase "Troxler effect" refers to an unexpected glare that a motorist recognizes. In this paper, we will provide an optimal solution to this challenge/Troxler effect. The primary objective of this paper is to design an internet of things (IoT)-based smart headlight control model. Our system introduced a cost-effective vehicle’s headlights controlled by light detection. According to this paper, a vehicle’s headlights are automatically rotated down when the sensor detects lights from the opposite direction of the vehicle headlights. We tried to reduce the road accident rate with our proposed system. This type of technology will prove useful in the motor vehicle sector and offer an innovative approach that ensures driver safety as well as increasing economic development.
A hybrid machine learning approach for improved ponzi scheme detection using advanced feature engineering Hossain, Fahad; Shuvo, Mehedi Hasan; Uddin, Jia
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 14, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v14i1.pp50-58

Abstract

Ponzi schemes deceive investors with promises of high returns, relying on funds from new investors to pay earlier ones, creating a misleading appearance of profitability. These schemes are inherently unsustainable, collapsing when new investments wane, leading to significant financial losses. Many researchers have focused on detecting such schemes, but challenges remain due to their evolving nature. This study proposes a novel hybrid machine-learning approach to enhance Ponzi scheme detection. Initially, we train an XGBoost classifier and extract its features. Meanwhile, we tokenize opcode sequences, train a gated recurrent unit (GRU) model on these sequences, and extract features from the GRU. By concatenating the features from the XGBoost classifier and the GRU, we train a final XGBoost model on this combined feature set. Our methodology, leveraging advanced feature engineering and hybrid modeling, achieves a detection accuracy of 96.57%. This approach demonstrates the efficacy of combining XGBoost and GRU models, along with sophisticated feature engineering, in identifying fraudulent activities in Ethereum smart contracts. The results highlight the potential of this hybrid model to offer more robust and accurate Ponzi scheme detection, addressing the limitations of previous methods.
Real-time IoT security framework for detecting a person with a weapon using Raspberry Pi, Google Vertex AI, and AWS Schutte, Storm; Uddin, Jia
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7627

Abstract

Realtime crime scene detection is a vital issue for ensuring security in various environments. Building on recent advancements in machine learning algorithms, this paper presents an IoT framework for real-time weapon and face detection. By deploying a convolutional neural network (CNN) architecture in Vertex AI and utilizing the portable camera module of a Raspberry Pi, to detect whether a person is carrying a weapon. This is achieved by pre-processing, which we resize and annotate the images. Then, train and validate the CNN model with the annotated label dataset. The trained model is saved in Google Cloud’s Vertex AI portal. Then we tested the model by uploading live images from a camera as well as a few video clips, to a Django application in amazon web hosting services (AWS) to Vertex AI. The model exhibited an accuracy of 97.2% along with a F1 score of 0.97. In addition, the model outperforms the other state-of-the-art models by less trainable parameters and higher accuracy.
A cost-effective counterfeiting prevention method using hashing, QR code, and website Hossain, Monir; Begum, Momotaz; Das, Bimal Chandra; Uddin, Jia
International Journal of Advances in Applied Sciences Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i2.pp351-359

Abstract

In this paper, we proposed a cost-effective software method to prevent counterfeiting where we used a website, quick-response (QR) code, and hashing. At the early stage of the product, the system will create a unique ID and a password with a random password generator for all products. Then, the password hash would be stored along with the ID in the database. At the same time, the password would be converted into a QR code for each product. The manufacturer will collect the QR code and ID and attach them to the product. When consumers attempt to verify the product, they will enter the website provided by the manufacturer and scan the QR code. After applying the same hash used before, the code will be checked on the database. After a successful check, the product entity will be destroyed and the life of the product ends. This paper contains flowcharts, figures, cost estimation, and a detailed explanation of the system. As it only requires domain hosting, thus the fixed cost of the system is so lower to bear for small enterprises also. We built a similar system using PHP, HTML, JavaScript for websites, and MYSQL for databases.
Real-time smoke and fire detection using you only look once v8-based advanced computer vision and deep learning Rahman, Shakila; Jamee, Syed muhammad Hasnat; Rafi, Jakaria Khan; Juthi, Jafrin Sultana; Sajib, Abdul Aziz; Uddin, Jia
International Journal of Advances in Applied Sciences Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v13.i4.pp987-999

Abstract

Fire and smoke pose severe threats, causing damage to property and the environment and endangering lives. Traditional fire detection methods struggle with accuracy and speed, hindering real-time detection. Thus, this study introduces an improved fire and smoke detection approach utilizing the you only look once (YOLO)v8-based deep learning model. This work aims to enhance accuracy and speed, which are crucial for early fire detection. The methodology involves preprocessing a large dataset containing 5,700 images depicting fire and smoke scenarios. YOLOv8 has been trained and validated, outperforming some baseline models- YOLOv7, YOLOv5, ResNet-32, and MobileNet-v2 in the precision, recall, and mean average precision (mAP) metrics. The proposed method achieves 68.3% precision, 54.6% recall, 60.7% F1 score, and 57.3% mAP. Integrating YOLOv8 in fire and smoke detection systems can significantly improve response times, enhance the ability to mitigate fire outbreaks, and potentially save lives and property. This research advances fire detection systems and establishes a precedent for applying deep learning techniques to critical safety applications, pushing the boundaries of innovation in public safety.
Co-Authors Begum, Momotaz Chakrabarty, Amitabha Chowdhury, Mohammad Asaduzzaman Das, Bimal Chandra Ferdoush, Zannatul Hossain, Fahad Hossain, Monir Hossen, Thofazzol Islam, Towhidul Jamee, Syed muhammad Hasnat Juthi, Jafrin Sultana Kashem, Mohammod Abul Mahmud, Booshra Nazifa Mustary, Shabnom Rafi, Jakaria Khan Rahman, Shakila Sajib, Abdul Aziz Schutte, Storm Shuvo, Mehedi Hasan Ullah, Nayeem