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Publicações

Publicações por Tahsir Ahmed Munna

2019

Prediction Model for Prevalence of Type-2 Diabetes Complications with ANN Approach Combining with K-Fold Cross Validation and K-Means Clustering

Autores
Munna, MTA; Alam, MM; Allayear, SM; Sarker, K; Ara, SJF;

Publicação
Lecture Notes in Networks and Systems - Advances in Information and Communication

Abstract

2018

Haar Cascade Classifier and Lucas–Kanade Optical Flow Based Realtime Object Tracker with Custom Masking Technique

Autores
Mohiuddin, K; Alam, MM; Das, AK; Munna, MTA; Allayear, SM; Ali, MH;

Publicação
Advances in Intelligent Systems and Computing - Advances in Information and Communication Networks

Abstract

2019

Prediction model for prevalence of type-2 diabetes complications with ann approach combining with K-fold cross validation and K-means clustering

Autores
Munna M.T.A.; Alam M.M.; Allayear S.M.; Sarker K.; Ara S.J.F.;

Publicação
Advances in Intelligent Systems and Computing

Abstract
In today’s era, most of the people are suffering with chronic diseases because of their lifestyle, food habits and reduction in physical activities. Diabetes is one of the most common chronic diseases which has affected to the people of all ages. Diabetes complication arises in human body due to increase of blood glucose (sugar) level than the normal level. Type-2 diabetes is considered as one of the most prevalent endocrine disorders. In this circumstance, we have tried to apply Machine learning algorithm to create the statistical prediction based model that people having diabetes can be aware of their prevalence. The aim of this paper is to detect the prevalence of diabetes relevant complications among patients with Type-2 diabetes mellitus. The processing and statistical analysis we used are Scikit-Learn, and Pandas for Python. We also have used unsupervised Machine Learning approaches known as Artificial Neural Network (ANN) and K-means Clustering for developing classification system based prediction model to judge Type-2 diabetes mellitus chronic diseases.

2018

A computational technique for intelligent computers to learn and identify the human's relative directions

Autores
Kabir S.; Allayear S.; Alam M.; Munna M.;

Publicação
Proceedings of the International Conference on Intelligent Sustainable Systems, ICISS 2017

Abstract
The most broadly perceived relative directions are right, left, up, down, backward and forward. This research paper presents a new computational technique to learn human's relative directions, where one intelligent computer can learn any human's right, left, up, down, backward and forward or different relative directions. The present paper portrays models describing the essential structures of relative direction learning process between human and intelligent machine. We developed two proficient algorithms for solving this approach. In our experiment we propose Human Relative Direction Learning (HRDL) algorithm for learning human's relative directions and Human Direction Identification (HDI) algorithm for tracking any human position and identity human's relative directions from different direction points.

2021

Cross-Domain Co-Author Recommendation Based on Knowledge Graph Clustering

Autores
Munna, TA; Delhibabu, R;

Publicação
INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2021

Abstract
Nowadays, due to the growing demand for interdisciplinary research and innovation, different scientific communities pay substantial attention to cross-domain collaboration. However, having only information retrieval technologies in hands might be not enough to find prospective collaborators due to the large volume of stored bibliographic records in scholarly databases and unawareness about emerging cross-disciplinary trends. To address this issue, the endorsement of the cross-disciplinary scientific alliances have been introduced as a new tool for scientific research and technological modernization. In this paper, we use a state-of-art knowledge representation technique named Knowledge Graphs (KGs) and demonstrate how clustering of learned KGs embeddings helps to build a cross-disciplinary co-author recommendation system. © 2021, Springer Nature Switzerland AG.

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