Browsing by Author "Rakshit, Aniruddha"

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An IoT Based System for Printing Braille Letter from Speech
(IEEE, 2020-06) Ahmed, Foysal; Choudhury, Abu Raihan; Rakshit, Aniruddha; Hasan, Md. Zahid
Visually impaired person cannot perform their activities like ordinary person. It becomes more difficult when a person is visually impaired along with deafness. Sometimes, it is quite impossible to communicate with such people whether in speaking or writing format. Therefore, Expert Braille Communicating System (EBCS) is a promising solution which provides braille writing format from voice. Unfortunately, people of underdeveloped countries are not getting the facilities of advanced electronic braille system for overpricing. In this work, an attempt has taken on designing a modified braille device which is economical and more compelling than previous. The device takes voice as input from an android apps and can convert the voice signal into text format of braille and prints the braille letter in paper. EBCS is trained with 1 epoch and the accuracy is achieved of 97.6%. By implementing the considered Braille system, visually impaired person can entirely engage themselves in the society.
Classification of Succulent Plant Using Convolutional Neural Network
(Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Springer, 2020-07-30) Das, Ashik Kumar; Iqbal, Md. Asif; Paul, Bidhan; Rakshit, Aniruddha; Hasan, Md. Zahid
Machine learning methods such as deep neural networks have remarkably improved plant species classification in recent years. It is very challenging task to classify plant species based on their categories. In this work, deep learning approach is explained to identify and classify succulent plant species using VGG19, three layers CNN and five layers CNN network on our dataset. The proposed architecture achieved a significant result from VGG19 and three layers CNN model. In succulent plant image dataset, there are 10 different classes of succulent and non-succulent plants. The dataset consists of 3632 succulent plant images and 200 non-succulent plant images. The model achieved 99.77% accuracy which performs better than VGG19 and three layers CNN model
Classification of Succulent Plant Using Convolutional Neural Network
(Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Springer, 2020-07-30) Hasan, Md. Zahid; Rakshit, Aniruddha; Das, Ashik Kumar; Iqbal, Md. Asif; Paul, Bidhan
Machine learning methods such as deep neural networks have remarkably improved plant species classification in recent years. It is very challenging task to classify plant species based on their categories. In this work, deep learning approach is explained to identify and classify succulent plant species using VGG19, three layers CNN and five layers CNN network on our dataset. The proposed architecture achieved a significant result from VGG19 and three layers CNN model. In succulent plant image dataset, there are 10 different classes of succulent and non-succulent plants. The dataset consists of 3632 succulent plant images and 200 non-succulent plant images. The model achieved 99.77% accuracy which performs better than VGG19 and three layers CNN model.
Computer Vision Based Skin Disorder Recognition Using Efficient net
(2021 International Conference on Information Technology (ICIT), IEEE, 2021-07-26) Hridoy, Rashidul Hasan; Akter, Fatema; Rakshit, Aniruddha
Skin disorders have a vital impact on people's health and quality of life, it is essential to develop a reliable and accurate computer vision approach to recognize multiple skin disorders. In this paper, a new rapid recognition approach using Efficient Net has been introduced to diagnose twenty types of skin disorders. Initially, image augmentation techniques have employed, and then eight architectures of Efficient Net between B0 and B7 have trained using the transfer learning approach. To evaluate the performance of models, different experimental studies have employed using a test set of 6300 images of skin disorders dataset that makes the proposed approach more reliable and accurate. EfficientNet-B7 has achieved the highest accuracy 97.10% among all architectures but has taken longer training time. EfficientNet-B0 has taken the lowest training time and has achieved 93.35% accuracy. EfficientNet-B7 has also taken the lowest time in recognizing unseen new images of skin disorders accurately than others.
Computer Vision Based Skin Disorder Recognition Using Efficientnet
(2021 International Conference on Information Technology (ICIT), IEEE, 2021-07-26) Hridoy, Rashidul Hasan; Akter, Fatema; Rakshit, Aniruddha
Skin disorders have a vital impact on people's health and quality of life, it is essential to develop a reliable and accurate computer vision approach to recognize multiple skin disorders. In this paper, a new rapid recognition approach using Efficient Net has been introduced to diagnose twenty types of skin disorders. Initially, image augmentation techniques have employed, and then eight architectures of Efficient Net between B0 and B7 have trained using the transfer learning approach. To evaluate the performance of models, different experimental studies have employed using a test set of 6300 images of skin disorders dataset that makes the proposed approach more reliable and accurate. EfficientNet-B7 has achieved the highest accuracy 97.10% among all architectures but has taken longer training time. EfficientNet-B0 has taken the lowest training time and has achieved 93.35% accuracy. EfficientNet-B7 has also taken the lowest time in recognizing unseen new images of skin disorders accurately than others
Image-Based Soft Drink Type Classification and Dietary Assessment System Using Deep Convolutional Neural Network with Transfer Learning
(Daffodil International University, 2020-09-09) Hafiz, Rubaiya; Haque, Mohammad Reduanul; Rakshit, Aniruddha; Uddin, Mohammad Shorif
Nowadays, people are taking soft drinks (carbonated nonalcoholic beverages) at an increasing rate. Health experts around the world have cautioned from time to time that these drinks lead to weight gain, raise the risk of non-communicable diseases, and so on. To develop consciousness among people, the present work describes an image-based tool to self-monitor the nutritional information of soft drinks by using a deep convolutional neural network (CNN) along with transfer learning. At first, a pre-processing function is done through noise reduction and contrast enhancement. Then the location of the drinks region is extracted through visual saliency and mean-shift segmentation technique. After removing backgrounds and segment out only the region of interest from the image a deep CNN-based transfer learning model is employed for the drink classification. Finally, the size of each drink bottle is estimated using the bag-of-feature (BoF) and distance ratio calculation to find the nutrition value from the nutrition fact table. To perform experimentation a dataset is built containing ten most consumed soft drinks in Bangladesh using images from the ImageNet dataset, internet sources and also self-capturing. The experiment confirms that our system can detect and recognize different types of drinks with an accuracy of 98.51%.
Image-based Soft Drink Type Classification and Dietary Assessment System Using Deep Convolutional Neural Network with Transfer Learning
(Journal of King Saud University - Computer and Information Sciences, 2020-09-09) Hafiz, Rubaiya; Rakshit, Aniruddha; Uddin, Mohammad Shorif; Haque, Mohammad Reduanul
Nowadays, people are taking soft drinks (carbonated nonalcoholic beverages) at an increasing rate. Health experts around the world have cautioned from time to time that these drinks lead to weight gain, raise the risk of non-communicable diseases, and so on. To develop consciousness among people, the present work describes an image-based tool to self-monitor the nutritional information of soft drinks by using a deep convolutional neural network (CNN) along with transfer learning. At first, a pre-processing function is done through noise reduction and contrast enhancement. Then the location of the drinks region is extracted through visual saliency and mean-shift segmentation technique. After removing backgrounds and segment out only the region of interest from the image a deep CNN-based transfer learning model is employed for the drink classification. Finally, the size of each drink bottle is estimated using the bag-of-feature (BoF) and distance ratio calculation to find the nutrition value from the nutrition fact table. To perform experimentation a dataset is built containing ten most consumed soft drinks in Bangladesh using images from the ImageNet dataset, internet sources and also self-capturing. The experiment confirms that our system can detect and recognize different types of drinks with an accuracy of 98.51%.
Predicting and Staging Chronic Kidney Disease of Diabetes (Type-2) Patient Using Machine Learning Algorithms
(International Journal of Innovative Technology and Exploring Engineering, Blue Eyes Intelligence Engineering & Sciences Publication, 2019-10-02) Basak, Setu; Alam, Md. Mahbub; Rakshit, Aniruddha; Marouf, Ahmed Al; Majumder, Anup
Mortality because of unending kidney disease increments essentially in recent years. Nowadays, about 422 million patients are suffering from diabetes among them around 30 percent of patients with Type 1 (adolescent beginning) diabetes and around 10 to 40 percent of those with Type 2 (grown-up beginning) diabetes in the end will experience the negative impacts of kidney damage. It is evident, that early detection of Chronic Kidney Disease (CKD) can mitigate the level of damage in the adulthood. In this paper, we have presented a comparative analysis based on the performance of five different algorithms-Naive Bayes (NB), In-stance Based Learning (IBK), Random Forest (RF), Decision Stump (DS) and Decision Tree (J48) for predicting CKD of diabetes patients only by urine test. Among all the algorithms the IBK gives the best result. Our comparison of different algorithms will help people with diabetes to find out if they are having CKD or not.
Processing With Patients’ Statements
(Scopus, 2020) Hossain, Shakhawat; Hasan, Md. Zahid; Rakshit, Aniruddha
This paper represents a novel strategy for developing a disease diagnosis gadget from a patient’s statement. For that, the system solely accepts patients’ statements in a natural language like English and analyzes the patients’ statements to prognosis the symptoms the affected person is presently suffering from. The framework forms the patients’ discourse and afterward utilizes Term Frequency (TF) to find the indications of a malady. Cosine Similarity is utilized to settle on a final decision with respect to regarding disease diagnosis task. Cosine Similarity quantifies the similitude between two non-zero vectors in a vector space model where one of the vectors is constructed with the symptoms the patient is encountering and the rest is developed during knowledge base setup. The framework is tested over 1013 patients with various ailments and its accuracy up to 98.3%.
Recognition of Jute Diseases by Leaf Image Classification Using Convolutional Neural Network
(10th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2019, IEEE, 2019-12-30) Hasan, Md. Zahid; Ahamed, Md. Sazzadur; Rakshit, Aniruddha; Hasan, K. M. Zubair
As Convolutional Neural Network (CNN) is achieving the state-of-the-art in the field of image classification, this research work focuses on the finding prominent accuracy of the jute leaf image diseases using deep learning approach. Acquiring the better performance in disease identification is the main purpose of this paper. Among different types of jute leaf diseases, Chlorosis and Yellow Mosaic have been selected to recognize the diseased leaves from the healthy leaves. As per our knowledge, no other method for leaf disease detection of Jute plant has been proposed for the first time. Using a dataset of 600 images, proposed model is aimed to classify two common jute leaf diseases. CNN achieves an overall accuracy of 96% without applying any image preprocessing and feature extraction method. The results suggest that proposed deep learning model provides an improved solution in disease control for jute leaf diseases with high accuracy.
Voice-Controlled Smart Assistant and Real-Time Vehicle Detection for Blind People
(Scopus, 2020) Akanda, Mojibur Rahman Redoy Md; Khandaker, Mohammad Masum; Saha, Tushar; Haque, Jahidul; Majumder, Anup; Rakshit, Aniruddha
The world is now like a global village with the help of modern technology. Normal people are taking huge facility from this modernization. Because of blindness and visual impairment, lot of people facing problems in their regular normal life. By using technology here proposed a system which will help them to make their life easier by giving instruction when they are outside from home. This system is totally voice controlled. Blind people can know the current location and can travel by walk and by bus to different places by using this system. They will get continuous instruction through speech which will ensure the service perfectly. In this paper, here explained their problems, related work, full process and model of the proposed system. This system helps them in their daily life to travel independently.