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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Jia Uddin
Woosong University
Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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An internet of things framework for real-time aquatic environment monitoring using an Arduino and sensors Md. Monirul Islam; Mohammad Abul Kashem; Jia Uddin
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i1.pp826-833

Abstract

Aquaculture is the farming of aquatic organisms in natural, controlled marine and freshwater environments. The real-time monitoring of aquatic environmental parameters is very important in fish farming. Internet of things (IoT) can play a vital role in the real-time monitoring. This paper presents an IoT framework for the efficient monitoring and effective control of different aquatic environmental parameters related to the water. The proposed system is implemented as an embedded system using sensors and an Arduino. Different sensors including pH, temperature, and turbidity, ultrasonic are placed in cultivating pond water and each of them is connected to a common microcontroller board built on an Arduino Uno. The sensors read the data from the water and store it as a comma-separated values (CSV) file in an IoT cloud named ThingSpeak through the Arduino microcontroller. To validate the experiment, we collected data from 5 ponds of various sizes and environments. After experimental evaluation, it was observed among 5 ponds, only three ponds were perfect for fish farming, where these 3 ponds only satisfied the standard reference values of pH (6.5-8.5), temperature (16-24 °C), turbidity (below 10 ntu), conductivity (970-1825 μS/cm), and depth (1-4) meter. At the end of this paper, a complete hardware implementation of this proposed IoT framework for a real-time aquatic environment monitoring system is presented.
Fish survival prediction in an aquatic environment using random forest model Md. Monirul Islam; Mohammod Abul Kashem; Jia Uddin
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 3: September 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i3.pp614-622

Abstract

In the real world, it is very difficult for fish farmers to select the perfect fish species for aquaculture in a specific aquatic environment. The main goal of this research is to build a machine learning that can predict the perfect fish species in an aquatic environment. In this paper, we have utilized a model using random forest (RF). To validate the model, we have used a dataset of aquatic environment for 11 different fishes. To predict the fish species, we utilized the different characteristics of aquatic environment including pH, temperature, and turbidity. As a performance metrics, we measured accuracy, true positive (TP) rate, and kappa statistics. Experimental results demonstrate that the proposed RF-based prediction model shows accuracy 88.48%, kappa statistic 87.11% and TP rate 88.5% for the tested dataset. In addition, we compare the proposed model with the state-of-art models J48, RF, k-nearest neighbor (k-NN), and classification and regression trees (CART). The proposed model outperforms than the existing models by exhibiting the higher accuracy score, TP rate and kappa statistics.
Hajj pilgrimage video analytics using CNN Md Roman Bhuiyan; Junaidi Abdullah; Noramiza Hashim; Fahmid Al Farid; Mohd Ali Samsudin; Norra Abdullah; Jia Uddin
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.2361

Abstract

This paper advances video analytics with a focus on crowd analysis for Hajj and Umrah pilgrimages. In recent years, there has been an increased interest in the advancement of video analytics and visible surveillance to improve the safety and security of pilgrims during their stay in Makkah. It is mainly because Hajj is an entirely special event that involve hundreds of thousands of people being clustered in a small area. This paper proposed a convolutional neural network (CNN) system for performing multitude analysis, in particular for crowd counting. In addition, it also proposes a new algorithm for applications in Hajj and Umrah. We create a new dataset based on the Hajj pilgrimage scenario in order to address this challenge. The proposed algorithm outperforms the state-of-the-art approach with a significant reduction of the mean absolute error (MAE) result: 240.0 (177.5 improvement) and the mean square error (MSE) result: 260.5 (280.1 improvement) when used with the latest dataset (HAJJ-Crowd dataset). We present density map and prediction of traditional approach in our novel HAJJ-crowd dataset for the purpose of evaluation with our proposed method.
A hybrid framework for detecting structured query language injection attacks in web-based applications Md. Hasan Furhad; Ripon K. Chakrabortty; Michael J. Ryan; Jia Uddin; Iqbal H. Sarker
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i5.pp5405-5414

Abstract

Almost every web-based application is managed and operated through a number of websites, each of which is vulnerable to cyber-attacks that are mounted across the same networks used by the applications, with much less risk to the attacker than physical attacks. Such web-based attacks make use of a range of modern techniques-such as structured query language injection (SQLi), cross-site scripting, and data tampering-to achieve their aims. Among them, SQLi is the most popular and vulnerable attack, which can be performed in one of two ways; either by an outsider of an organization (known as the outside attacker) or by an insider with a good knowledge of the system with proper administrative rights (known as the inside attacker). An inside attacker, in contrast to an outsider, can take down the system easily and pose a significant challenge to any organization, and therefore needs to be identified in advance to mitigate the possible consequences. Blockchain-based technique is an efficient approach to detect and mitigate SQLi attacks and is widely used these days. Thus, in this study, a hybrid method is proposed that combines a SQL query matching technique (SQLMT) and a standard blockchain framework to detect SQLi attacks created by insiders. The results obtained by the proposed hybrid method through computational experiments are further validated using standard web validation tools.
Machine Learning based Stream Selection of Secondary School Students in Bangladesh Shabbir Ahmad; Md. Golam Rabiul Alam; Jia Uddin; Md Roman Bhuiyan; Tasnim Sakib Apon
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 11, No 1: March 2023
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v11i1.4302

Abstract

In the Bangladeshi education system, there are three stages up to the secondary school certificate (SSC)- the primary (Primary Education Completion Certificate, or PEC), middle school (Junior School Certificate, or JSC), and SSC. A separate stream has to be chosen after the eighth grade, which could be any of the following streams: Science, Business Studies, and Humanities. The selection of a stream is very important for their future higher studies and career planning. Usually, students take the decision of selecting a stream based on PSC and JSC results only. To address this challenge, we have collected a dataset from different Bangladeshi schools, which consists of PSC and JSC students' records. There are 26 data for each student record including subject-wise student results, parent’s academic qualification, parent’s profession, parent’s monthly income, sibling information, district, etc. In the experimental analysis, a series of machine learning regression algorithms have been utilized. Moreover, we have employed various performance metrics in order to validate our model’s performance. The experimental results demonstrate that among the regressors, extreme gradient boosting algorithm’s performance were superior in both science and humanities streams. In the business stream however, Support Vector Machine’s performance is considerably better. It is expected that the analysis will help prospective students and stakeholders in their future decisions. Moreover, we have utilized Local Interpretable Model Agnostic Explanations that helps to increase the interpretability of the model.
Real-time smoke and fire detection using you only look once v8-based advanced computer vision and deep learning Shakila Rahman; Syed muhammad Hasnat Jamee; Jakaria Khan Rafi; Jafrin Sultana Juthi; Abdul Aziz Sajib; Jia Uddin
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.pp991-1003

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.
Predicting Indonesian academician turnover intention: validity and reliability analysis Faisal Al Abid; Aryati Bakri; Hasin Jawad Ali; Darmawan Satyananda; Shefayatuj Johara Chowdhury; Jia Uddin
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp479-489

Abstract

This study evaluates Indonesian academic turnover intention (TOI) by analyzing demographic and work-related factors through feature selection methods and utilizes random forest (RF) as a baseline classifier for TOI prediction, while applying statistical methods to ensure the reliability of the collected primary dataset. The main advantage of this approach is to find out the importance of these factors with statistical validation to reliably investigate Indonesian academicians’ TOI. Feature selection methods such as information gain (IG) and SelectKBest were used to find out feature importance, while the reliability of the dataset was assessed through statistical approaches such as Cronbach alpha, confirmatory factor analysis (CFA), average variance extracted (AVE), and consistency ratio (CR). To test the importance of demographic and work-related factors, Python was used as an implementation tool for the Indonesian academic TOI dataset (IRB reference: 19.12.4/UN32.14/PB/2024), comprising 527 samples. The superiority of the importance of work-related factors in contrast to demographic factors was consistently demonstrated by feature selection methods, and a statistical approach confirmed the reliability of the collected primary dataset, consequently ensuring the robustness of the findings. It is envisaged that this approach can be very useful for human resource (HR) departments to pay more attention to the important demographic factors for reducing Indonesian academic TOI.
Comparative analysis of YOLO variants and EfficientNet for detecting bone fractures in X-ray images Shatabdi Sarker; Avizit Roy; Shaila Sharmin; Shakila Rahman; Jia Uddin
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp155-167

Abstract

A bone fracture is a serious medical problem, and accurate and prompt diagnosis is crucial for optimal treatment. This study highlights the progress of automatic bone fracture detection using deep learning (DL) models. A dataset containing 17 different fracture classes was used to train and evaluate the models. The dataset had class imbalance and minor fracture detection challenges. Extensive preprocessing, including data augmentation and resizing, has been applied to solve these problems, which has helped to increase the robustness of the model. Seven state-of-the-art models—you only look once (YOLO)v8, YOLOv9, YOLOv10, YOLOv11, EfficientNetB0, DenseNet169 and ResNet50—are trained and evaluated. Precision, recall, F1-score, and mean average precision (mAP) were used to evaluate the performance of the models. Among all models, YOLOv11 leads the others by achieving the highest precision, mAP, and precision-recall balance. YOLOv11 adds architectural improvements such as a deep backbone network and hybrid feature fusion, which make the model more reliable in different types of fracture detection. It is capable of reducing false detections and maintaining stable memory usage consistency even under different imaging conditions. Overall, YOLOv11 showed promising results and highlighted the potential of AI-powered diagnostic tools to improve clinical processes and patient care. As future work, the application field of the model can be extended to larger medical imaging tasks, and it can be further refined for effective use in resource-limited environments.
A Real-time Robust English and Bangla License Plate Recognition Using Enhanced GAN and Explainable CNN Jul Jalal Al-Mamur Sayor; Nishat Tasnim Shishir; Mahe Zabin; Suman Saha; Kamruddin Nur; Jia Uddin
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 2: June 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i2.6823

Abstract

Efficient systems tailored to countries with bilingual license plates remain scarce, and existing methods often struggle to adapt the unique challenges posed by these scenarios. Countries like Bangladesh and India face challenges with the existing techniques. Accurate recognition of bilingual license plates is crucial for traffic management, but conventional methods with limited resources often fail to work effectively with low-quality footage. Thus, this research provides a framework for the resource-efficient solution for bilingual number plates, ESRGAN for resolution enhancement and explainable CNNs with transfer learning for language classification and character recognition. We divide the task into two sub-tasks and train four specialized CNN models for Bangla and English characters and numerals to maximize the system’s performance. The proposed two-tier architecture optimizes computational efficiency by dynamically loading models based on recognition requirements. Overall, using the gradient-based explainability techniques in the proposed framework increases the credibility of decision-making. The proposed models achieved accuracies of 96%, 97.48%, 97.15%, and 97.62% for English license plate recognition, Bangla license plate recognition, Bangla number recognition, and English number recognition, respectively. While maintaining competitive accuracy, the proposed approach outperforms state-of-the-art works in resource efficiency and computational time, which are crucial for edge deployments. Furthermore, the license plate detection and character extraction algorithm proposed in the paper demonstrates excellent accuracy for both black and white-colored plates. This approach ensures consistent performance and efficient resource utilization while showing the ability to work with a variety of traffic conditions, making it suitable for automated vehicle management systems.
Co-Authors Abdul Aziz Sajib Aryati Bakri Avizit Roy Darmawan Satyananda Fahmid Al Farid Faisal Al Abid Hasin Jawad Ali Iqbal H. Sarker Jafrin Sultana Juthi Jakaria Khan Rafi Jul Jalal Al-Mamur Sayor Junaidi Abdullah Kamruddin Nur Mahe Zabin Md Roman Bhuiyan Md. Golam Rabiul Alam Md. Hasan Furhad Md. Monirul Islam Md. Monirul Islam Michael J. Ryan Mohammad Abul Kashem Mohammod Abul Kashem Mohd Ali Samsudin Nishat Tasnim Shishir Noramiza Hashim Norra Abdullah Ripon K. Chakrabortty Shabbir Ahmad Shaila Sharmin Shakila Rahman Shakila Rahman Shatabdi Sarker Shefayatuj Johara Chowdhury Suman Saha Syed muhammad Hasnat Jamee Tasnim Sakib Apon