Browsing by Author "Rimi, Iffat Firozy"

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Risk of Dental Disease Prediction Using Machine Learning
(Daffodil International University, 2020-10-08) Rimi, Iffat Firozy; Akter, Sharmin
Now a day’s dental disease is the major health problem in Bangladesh. So dental care is important to most people in our country. But the cost of dental care services is increasing day by day. We will predict the risk of dental disease with machine learning. We identify the most common disease among people, consult with a dentist about those diseases, reading-related journals, and online articles, we find out the habitats that cause dental disease. Then we collect data based on those factors, such as age, brush before sleep, brush after eating morning, eating chocolates, soft drinks, betel leaf/nut, etc. We collect data from both those who have already a disease and those who don’t. We have two outcomes. One is ‘Yes’ meaning they have dental disease and another is ‘No’ means they don’t have dental disease. We apply machine-learning algorithms to our processed dataset. Recently machine learning, artificial intelligence, and deep learning used in various predictions and detection systems. We use k-nearest neighbor (KNN), logistic regression, support vector machine (SVM), naïve Bayes, random forest, adaptive boosting (ADA boosting), decision tree, multilayer perceptron (MLP-ANN), Linear Discriminant Analysis (LDA), and gradient boosting classifier. In our work, we use those factors answer as input and after processing and applying the algorithm, we find out addicted or not addicted as our output with the accuracy of 95.89% on the logistic regression algorithm.
SweetSight: A Deep Convolutional Neural Network Approach for Automatic Categorization of Bengal Sweets
(Scopus, 2024-08-20) Supriya, Soummo; Rimi, Iffat Firozy; Islam, Md. Moinul; Rahman, Md. Sadekur; Nawshin, Samia; Habib, Md. Tarek
The manufacture of a wide variety of sweets is on the rise in the entire Bengal (both Bangladesh and West Bengal). As a consequence, the sweet’s name escapes the vast majority of individuals in our country. Computer vision advancements have made object recognition from photos easier in recent years. Using computer vision to automatically categorize sweets is still a challenge because of the similarity between various sorts and characteristics such as their placement or lighting conditions. Classifying sweets may be useful in a variety of domains, including autonomous economic robots and the creation of mobile apps for identifying certain sweets on the market. In this article, we employed deep convolutional neural network (DCCN) methods to evaluate five alternative models for sweet detection. The endemic Bengali delicacies we used to train my model included Inception-v3, ResNet-50, VGG15, AlexNet, and CNN. This model was efficient. Our dataset comprised images of confections from thirteen distinct sweet categories. Two portions of the dataset were separated: 80% for training and 20% for testing. The training dataset was enhanced and increased to make preparation simpler. Using the Inception-v3 model, we were able to attain a 100% accuracy rate with our dataset.
SweetSight: A Deep Convolutional Neural Network Approach for Automatic Categorization of Bengal Sweets
(Springer Nature, 2024-08-20) Supriya, Soummo; Rimi, Iffat Firozy; Moinul Islam, Md.; Rahman, Md. Sadekur; Nawshin, Samia; Habib, Md. Tarek
The manufacture of a wide variety of sweets is on the rise in the entire Bengal (both Bangladesh and West Bengal). As a consequence, the sweet’s name escapes the vast majority of individuals in our country. Computer vision advancements have made object recognition from photos easier in recent years. Using computer vision to automatically categorize sweets is still a challenge because of the similarity between various sorts and characteristics such as their placement or lighting conditions. Classifying sweets may be useful in a variety of domains, including autonomous economic robots and the creation of mobile apps for identifying certain sweets on the market. In this article, we employed deep convolutional neural network (DCCN) methods to evaluate five alternative models for sweet detection. The endemic Bengali delicacies we used to train my model included Inception-v3, ResNet-50, VGG15, AlexNet, and CNN. This model was efficient. Our dataset comprised images of confections from thirteen distinct sweet categories. Two portions of the dataset were separated: 80% for training and 20% for testing. The training dataset was enhanced and increased to make preparation simpler. Using the Inception-v3 model, we were able to attain a 100% accuracy rate with our dataset.