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Publications

“Factors causing stunting among under five children in Bangladesh.”

Abstract:Malnutrition is one of the major problems in developing countries including Bangladesh. Stunting is a chronic malnutrition, which indicates low height for age and interrupt the growth. This study was conducted to find out the factors associated with the malnutrition status and test the accuracy of the algorithms which algorithm is performing well. Data from Bangladesh Demographic Health Survey (BDHS), 2014 is used. Factors like demographic, socioeconomic and environmental have differential influence on stunting. Based on analysis, about 36% of under-five age children were suffering from stunting. Decision Tree algorithm was applied to find the associated factors with stunting. It is found that mothers’ education, birth order number, economic status was associated with stunting. SVM (Support Vector Machine), ANN (Artificial Neural Network) is also applied with the stunting dataset to test the accuracy. The accuracy of Decision Tree 74%, SVM 76% and ANN 73%.
More Info: https://www.springerprofessional.de/en/factors-causing-stunting-among-under-five-children-in-bangladesh/18509292

A Machine Learning Approach to Predict SEER Cancer

Abstract:The SEER database is among the persuading stores regarding malignancy pointers inside us. The SEER list helps impact investigation for the gigantic measure of patients’ bolstered viewpoints for the most part ordered as an insightful segment and impact. Assistant careful proof nearly the carcinoma dataset is ordinarily started on the site of the National Cancer Institute. The main point of this work is that depending on the individual’s manifestations, and we will foresee whether individuals are in danger of malignant growth or not. Perseverance and desire for the benefit of malignant growth patients have the option to upsurge prophetic exactitude and limit in the end cause better-educated decisions. To the current end, various amendments smear AI to disease data of the surveillance, epidemiology, and end results database. It may be used to better forecast cancer in the medical sector, and these studies can give a good chance to enhance existing models and build new models for uncommon cancers of minority groups in particular. In this paper, the authors contribute to getting more predicted accuracy for SEER cancer and use it to better forecast cancer in the medical sector.
Keywords: SEER cancer, Carcinoma, Epidemiology, Machine learning, Data mining
More Info: https://link.springer.com/chapter/10.1007/978-981-19-1610-6_7

“Transfer Learning in Deep Reinforcement Learning.”

Abstract:Reinforcement learning has quickly risen in popularity because of its simple, intuitive nature, and its powerful results. In this paper, we study a number of reinforcement learning algorithms, ranging from asynchronous q-learning to deep reinforcement learning. We focus on the improvements they provide over standard reinforcement learning algorithms, as well as the impact of initial starting conditions on the performance of a reinforcement learning agent.Keywords: Deep learning, Transfer learning, Reinforcement learning, Convolutional neural networks, Q-networks
More Info: https://link.springer.com/chapter/10.1007/978-981-19-1607-6_13

Automatic Vehicle Bangla License Plate Detection and Recognition

Abstract:Automated license plate recognition is important in many contexts like security and law enforcement, monitoring vehicles, automated parking control, etc. To enable these automated services in Bangladesh, we are reviewing and combining several established methods in this paper. The task of recognizing vehicle number plates consists of three stages: plate detection, plate extraction, and character recognition. Each stage has many sub-steps. For every sub-step, we have reviewed many methods and chosen the one that proved to be the best solution after thorough testing and observation. The main objective of this research is to gain high accuracy using as little CPU Time as possible, keeping into consideration the facts like lighting conditions, vehicle motion, noisy plates, and segmented words in the input image. The primary target of this thesis is to extract a clean image of license plates of private or community vehicles. Although we target our system to be able to detect standard license plates, we also tested our methods on non-standard plates. We have gained an overall accuracy of 98.41% in plate detection, 82.25% in plate extraction, and 94.11% in character segmentation, resulting in 76.19% overall accuracy using the available dataset. In terms of efficiency, the runtime of our plate detection module is 0.841 s, plate extraction is 0.132 s, character segmentation required 0.009 s, and the average total runtime of the entire system is 0.982 s, in our 64-bit machine having 4.00 GB main memory and Intel(R) Core (TM) i3-4005U CPU @ 2 × 1.70. In this paper, our aim is to process the input image up to Character Segmentation so that it is easy to recognize the character by sending it to any existing Optical Character Recognition (OCR) system.
Keywords: Digital image processing, Automated license plate recognition (ALPR), Automated number plate recognition (ANPR), Bangla number plate, Number plate detectionMore Info: https://link.springer.com/chapter/10.1007/978-981-19-3311-0_44