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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.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 Abdul Aziz Sajib Fahmid Al Farid Iqbal H. Sarker Jafrin Sultana Juthi Jakaria Khan Rafi Junaidi Abdullah 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 Noramiza Hashim Norra Abdullah Ripon K. Chakrabortty Shabbir Ahmad Shakila Rahman Syed muhammad Hasnat Jamee Tasnim Sakib Apon