Browsing by Author "Saifuzzaman, Mohd"

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Real-Time Medical Image Classification with ML Framework and Dedicated CNN–LSTM Architecture
(Hindawi Publications, 2022) Salehin, Imrus; Islam, Md. Shamiul; Amin, Nazrul; Baten, Md. Abu; Noman, S. M.; Saifuzzaman, Mohd; Yazmyradov, Serdar
In the domain of modern deep learning and classification techniques, the convolutional neural network (CNN) stands out as a highly successful and preferred method for image classification in artificial intelligence. Especially in the medical field, CNN has proven to be an ideal approach for analyzing medical data and accurately identifying diseases. Over the recent years, CNN has demonstrated significant potential and success in various computer vision tasks, with medical image classification being one of the prominent applications. In our study, we introduce a novel custom CNN model called MedvCNN, designed for classifying different types of classes. We conduct experiments with various image sizes to explore their versatility. In addition, long short-term memory (LSTM), a type of recurrent neural network (RNN), is incorporated into our approach. LSTM is specifically tailored to handle sequential data, making it ideal for time series analysis. However, its capabilities extend beyond time series data and are effectively applied to various sequential data types, including sequential vectors derived from image data. One of the key advantages of utilizing LSTM for image classification is its ability to effectively memorize and capture important features in the image data. This feature is particularly advantageous in medical image processing, where precise and accurate identification of key attributes is crucial for successful diagnosis and analysis. Furthermore, our experiments reveal that the hybrid custom LSTM model, MedvLSTM, a RNN algorithm, surpasses other methods in the domain of medical image classification. Our study places significant emphasis on attaining robust classification performance for medical image data through a sophisticated, parameter free approach, complemented by an ablation study, and comprehensive statistical analysis. This comprehensive analysis and evaluation allow us to gain a deeper understanding of the model’s effectiveness and its potential impact in the field of medical image analysis. We compare these two approaches to a baseline CNN architecture, aiming to streamline the classification process, reduce time consumption, and improve cost efficiency. Additionally, we present a real-time web-based AutoML framework along with a practical demonstration. Ultimately, our research provides a thorough investigation of the current state-of-the-art in medical image analysis accuracy, focusing on the utilization of neural networks and LSTM.
Student’s Performance Prediction Using Data Mining Technique Depending On Overall Academic Status and Environmental Attributes
(Springer, 2021) Shetu, Syeda Farjana; Saifuzzaman, Mohd; Moon, Nazmun Nessa; Sultana, Sharmin; Yousuf, Ridwanullah
In the education sector, it has been a challenging task to identify the students individually to take appropriate actions to get a very deserving outcome from them. On the other hand, a student getting higher education should have the knowledge about the market demands and where are their weaknesses. If it is possible to get some data from students’ academic record and their percepts on some factors related to academic performances those may help to understand the reasons for success and failure which would be very useful in the educational environment and student success rate. We collect data from students from different institutes. First, we create an online survey form to get data, and then we process them to get some valuable information. After getting those data, we visualize then analyze them from different prospects. We try to get some exact knowledge which can be crucial for students’ success or failure in an academic environment. We apply the data mining technique decision tree algorithm (j48) to develop a model that shows us the hierarchy of different attributes related to students’ academic performance and their personal behaviors that affect a student’s academic status. Then we try to get information about which attributes have a positive and which attribute has a negative impact on students’ academic growth.