Browsing by Author "Bhowmik, Shohag Kumar"

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An Efficient and Optimized Tracking Framework through Optimizing Algorithm in a Deep Forest using NFC
(Indonesian Journal of Electrical Engineering and Computer Science, 2020) Khan, Md. Abbas Ali; Ali, Mohammad Hanif; Haque, A.K.M Fazlul; Debnath, Chandan; Bhowmik, Shohag Kumar
NFC is applying in various field of contemporary technology. Especially of convenience tag usability in any place. One of the facilities which can be added in the tracking system is the implementation of Near Field Communication in order to guide each tourist in the deep forest or any other location. In the deep forest, tracking or location detection activities need to be done efficiently, like desired path finding in a deep forest. At present, the tracking procedure in deep forest is working with the help of guides or local citizens. Currently, in any restricted area such as the “Sundarban” forest, no outside general people are allowed to travel in the jungle without any authorized guide which is not an efficient way to travel smoothly. The use of Near Field Communication can solve the problem related to lost the way, safety, and easily help the travelers to track the desired destination without the help of human resources or any guide. The NFC tags that hold mapping information of the area, in the point of tag setup all tags will be set up on several trees along with sequence.
Comparative Analysis of Human Face Recognition Using SURF and Neural Network Methods
(Scopus, 2021) Shamrat, F.M. Javed Mehedi; Bhowmik, Shohag Kumar; Muntasim, Mst. Fahmida; Nibir, Tafsirul Islam; Chowdhury, Tahmid Rashik; Thapa, Sittal
In computer vision, facial recognition technology is used to recognize every person. This approach is a revolution that is perfect for analyzing a graphic image or a video frame specially or differently. There are, however, systemic methods where facial appreciation schemes initiative, typically, and the effort by comparing chosen facial features from a specific image with faces in a database. It's also known as a Biometric Artificial Intelligence-based app that can see a person in extraordinary detail by dissecting structures related to their facial exteriors and figures. The professional employs a variety of techniques in order to complete the mission. The SURF and Neural Network methods are two of these methods. The writers of this paper address the methods mentioned above and how they operate. The emphasis of the debate is on the methods' accuracy rates and determining which approach produces the most reliable outcome based on facial image results.
Comparative Analysis to identify the best Classifier for Parkinson Prediction
(Scopus, 2021) Shamrat, F.M. Javed Mehedi; Bhowmik, Shohag Kumar; Sultana, Zakia; Hossain, Ahbab; Amina, Mahdia; Thapa, Sittal
Parkinson disease has become one of the most common diseases among people over the age of 65. Neurodegenerative disease affects movement, speech and other cognitive abilities. Among patients, the symptoms vary at a different rate, for which diagnosis of the disease sometimes takes years by when treatment is no longer an option. However, using machine learning algorithms to classify the symptoms among patients, it is possible for early detection of the disease. İn this paper, the performance of machine learning algorithms are measures that can detect Parkinson disease. Three different datasets are used for the study. Each dataset goes through various feature selection techniques. Machine learning classifiers such as KNN, LDA, NB, LR, SVM, DT, RT, RF and ANN are implemented on the datasets and their performance is measured. It is observed that SVM has a high accuracy rate of prediction over all the feature selection techniques in all the datasets.
SkinNet-16
(Daffodil International University, 2022-08-18) Ghosh, Pronab; Azam, Sami; Quadir, Ryana; Karim, Asif; Shamrat, F. M. Javed Mehedi; Bhowmik, Shohag Kumar; Jonkman, Mirjam; Hasib, Khan Md.; Ahmed, Kawsar
Skin cancer these days have become quite a common occurrence especially in certain geographic areas such as Oceania. Early detection of such cancer with high accuracy is of utmost importance, and studies have shown that deep learning- based intelligent approaches to address this concern have been fruitful. In this research, we present a novel deep learning- based classifier that has shown promise in classifying this type of cancer on a relevant preprocessed dataset having important features pre-identified through an effective feature extraction method. Skin cancer in modern times has become one of the most ubiquitous types of cancer. Accurate identification of cancerous skin lesions is of vital importance in treating this malady. In this research, we employed a deep learning approach to identify benign and malignant skin lesions. The initial dataset was obtained from Kaggle before several preprocessing steps for hair and background removal, image enhancement, selection of the region of interest (ROI), region-based segmentation, morphological gradient, and feature extraction were performed, resulting in histopathological images data with 20 input features based on geometrical and textural features. A principle component analysis (PCA)-based feature extraction technique was put into action to reduce the dimensionality to 10 input features. Subsequently, we applied our deep learning classifier, SkinNet-16, to detect the cancerous lesion accurately at a very early stage. The highest accuracy was obtained with the Adamax optimizer with a learning rate of 0.006 from the neural network-based model developed in this study. The model also delivered an impressive accuracy of approximately 99.19%.