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All Journal Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics
Md. Jueal Mia
Daffodil International University
Computer Science & IT Electrical & Electronics Engineering

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An automated navigation system for blind people Md. Atiqur Rahman; Sadia Siddika; Md. Abdullah Al-Baky; Md. Jueal Mia
Bulletin of Electrical Engineering and Informatics Vol 11, No 1: February 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i1.3452

Abstract

Proper navigation and detailed perception in familiar or unfamiliar environments are the main roles for human life. Eyesight sense helps humans to abstain from all kinds of dangers and navigate to indoor and outdoor environments. These are challenging activities for blind people in all environments. Many assistive tools have been developed by the blessing of technology like braille compasses and white canes that help them to navigate around in the environment. A vision and cloud-based navigation system for the visually impaired or blind person was developed. Our aim was not only to navigate them but also to perceive the environment in as much detail as a normal person. The proposed system includes ultrasonic sensors detecting obstacles, stereo camera to capture videos to perceive the environment using deep learning algorithms. Face recognition approach identified known faces in front of him. Blind people interacted with the whole system through a speech recognition module and all the information was stored in the cloud. Web and android applications were developed to track blinds so that guardians were monitoring them while visiting and reached them in an emergency. The experimental results showed the proposed system could provide more plenty information and user-friendly interaction.
Prediction of internet user satisfaction levels in Bangladesh using data mining and analysis of influential factors Md. Hasan Imam Bijoy; Sumiya Alam Akhi; Md. Ali Ashraf Nayeem; Md. Mahbubur Rahman; Md. Jueal Mia
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3617

Abstract

Today the world has already acknowledged as a global village by the inter-net which has technologically evolved into a significant performance instrument for individuals, businesses, and countries seeking to achieve betterment. This study is based on data mining techniques to predict the satisfaction level of internet users from the context of Bangladesh. After conducting a public survey with 18 questions, we were able to acquire 451 responses from participants. Data for user satisfaction was associated with end-user characteristics including certain getting high speed, internet packages, cable type of Wi-Fi connection with targeting various age groups and occupations. The research's most key conceptual breakthrough was the reliability of magnitude predictions of user satisfaction level based on their experience with internet use. The empirical findings indicate that people in Bangladesh have high expectations in existing internet technology, and they are very dissatisfied with their facilities of internet use and to measure satisfaction level related with monthly limit of the Wi-Fi packages and the elements affecting internet speed. Several classifier models were applied to our dataset and among them, Random Forest (RF) performance reaches the top position with 91.53% accuracy.
Cucumber disease recognition using machine learning and transfer learning Md. Jueal Mia; Syeda Khadizatul Maria; Shahrun Siddique Taki; Al Amin Biswas
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3096

Abstract

Cucumber is grown, as a cash crop besides it is one of the main and popular vegetables in Bangladesh. As Bangladesh's economy is largely dependent on the agricultural sector, cucumber farming could make economic and productivity growth more sustainable. But many diseases diminish the situation of cucumber. Early detection of disease can help to stop disease from spreading to other healthy plants and also accurate identifying the disease will help to reduce crop losses through specific treatments. In this paper, we have presented two approaches namely traditional machine learning (ML) and CNN-based transfer learning. Then we have compared the performance of the applied techniques to find out the most appropriate techniques for recognizing cucumber diseases. In our ML approach, the system involves five steps. After collecting the image, pre-processing is done by resizing, filtering, and contrast-enhancing. Then we have compared various ML algorithms using k-means based image segmentation after extracted 10 relevant features. Random forest gives the best accuracy with 89.93% in the traditional ML approach. We also studied and applied CNN-based transfer learning to investigate the further improvement of recognition performance. Lastly, a comparison among various transfer learning models such as InceptionV3, MobileNetV2, and VGG16 has been performed. Between these two approaches, MobileNetV2 achieves the highest accuracy with 93.23%.
Common human diseases prediction using machine learning based on survey data Jabir Al Nahian; Abu Kaisar Mohammad Masum; Sheikh Abujar; Md. Jueal Mia
Bulletin of Electrical Engineering and Informatics Vol 11, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i6.3405

Abstract

In this era, the moment has arrived to move away from disease as the primary emphasis of medical treatment. Although impressive, the multiple techniques that have been developed to detect the diseases. In this time, there are some types of diseases COVID-19, normal flue, migraine, lung disease, heart disease, kidney disease, diabetics, stomach disease, gastric, bone disease, autism are the very common diseases. In this analysis, we analyze disease symptoms and have done disease predictions based on their symptoms. We studied a range of symptoms and took a survey from people in order to complete the task. Several classification algorithms have been employed to train the model. Furthermore, performance evaluation matrices are used to measure the model's performance. Finally, we discovered that the part classifier surpasses the others.
An automated approach for eggplant disease recognition using transfer learning Izazul Haque Saad; Md. Mazharul Islam; Isa Khan Himel; Md. Jueal Mia
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.3575

Abstract

In Bangladesh, eggplant is a widely grown crop that is vital to the country’s food security. The vegetable is consumed on a regular basis by the majority of people. Since Bangladesh’s economy is heavily reliant on agriculture, eggplant growing might help the country’s economy and productivity flourish more efficiently. It provides several health benefits, including reducing cancer risk, blood sugar control, heart health, eye health, and others. Although eggplant is a valuable crop, it is subject to severe diseases that reduce its productivity. It’s hard to detect those diseases manually and needs a lot of time and hard work. So, we introduce an agricultural and medical expert system based on machine vision that analyzes a picture acquired with a smartphone or portable device and classifies diseases to assist farmers in resolving the issue. We studied and used a convolutional neural network (CNN)-based transfer learning approach for identifying eggplant diseases in this paper. We have used various transfer learning models such as DenseNet201, Xception, and ResNet152V2. DenseNet201 had the highest accuracy of these models with 99.06%.
Tumor-Net: convolutional neural network modeling for classifying brain tumors from MRI images Abu Kowshir Bitto; Md. Hasan Imam Bijoy; Sabina Yesmin; Imran Mahmud; Md. Jueal Mia; Khalid Been Badruzzaman Biplob
International Journal of Advances in Intelligent Informatics Vol 9, No 2 (2023): July 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i2.872

Abstract

Abnormal brain tissue or cell growth is known as a brain tumor. One of the body's most intricate organs is the brain, where billions of cells work together. As a head tumor grows, the brain suffers damage due to its increasingly dense core. Magnetic resonance imaging, or MRI, is a type of medical imaging that enables radiologists to view the inside of body structures without the need for surgery. The image-based medical diagnosis expert system is crucial for a brain tumor patient. In this study, we combined two Magnetic Resonance Imaging (MRI)-based image datasets from Figshare and Kaggle to identify brain tumor MRI using a variety of convolutional neural network designs. To achieve competitive performance, we employ several data preprocessing techniques, such as resizing and enhancing contrast. The image augmentation techniques (E.g., rotated, width shifted, height shifted, shear shifted, and horizontally flipped) are used to increase data size, and five pre-trained models employed, including VGG-16, VGG-19, ResNet-50, Xception, and Inception-V3. The model with the highest accuracy, ResNet-50, performs at 96.76 percent. The model with the highest precision overall is Inception V3, with a precision score of 98.83 percent. ResNet-50 performs at 96.96% for F1-Score. The prominent accuracy of the implemented model, i.e., ResNet-50, compared with several earlier studies to validate the consequence of this introspection. The outcome of this study can be used in the medical diagnosis of brain tumors with an MRI-based expert system.
Co-Authors Abu Kaisar Mohammad Masum Abu Kowshir Bitto Al Amin Biswas Imran Mahmud Isa Khan Himel Izazul Haque Saad Jabir Al Nahian Khalid Been Badruzzaman Biplob Md. Abdullah Al-Baky Md. Ali Ashraf Nayeem Md. Atiqur Rahman Md. Hasan Imam Bijoy Md. Mahbubur Rahman Md. Mazharul Islam Sabina Yesmin Sadia Siddika Shahrun Siddique Taki Sheikh Abujar Sumiya Alam Akhi Syeda Khadizatul Maria