Iqbal H. Sarker
In the current age of the Fourth Industrial Revolution (4 IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corre...
Iqbal H. Sarker
Abstract Artificial intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence into machines or systems. Thus, AI-based modeling is the key to build automated, intelligen...
Iqbal H. Sarker
The digital world has a wealth of data, such as internet of things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can ...
A B M Moniruzzaman, Syed Akhter Hossain
Digital world is growing very fast and become more complex in the volume (terabyte to petabyte), variety (structured and un-structured and hybrid), velocity (high speed in growth) in nature. This refers to as Big Data that is a global phenomenon. This is typically considered to be a data collection ...
Dewan Md. Farid, Li Zhang, Alamgir Hossain, Chowdhury Mofizur Rahman et al.
Iqbal H. Sarker
Real-life mobile phone data may contain noisy instances, which is a fundamental issue for building a prediction model with many potential negative consequences. The complexity of the inferred model may increase, may arise over-fitting problem, and thereby the overall prediction accuracy of the model...
Md. Zubair, Md Asif Iqbal, Avijeet Shil, Mohammad Jabed Morshed Chowdhury et al.
K-means algorithm is one of the well-known unsupervised machine learning algorithms. The algorithm typically finds out distinct non-overlapping clusters in which each point is assigned to a group. The minimum squared distance technique distributes each point to the nearest clusters or subgroups. One...
Md. Nawab Yousuf Ali, Md. Golam Sarowar, Md. Lizur Rahman, Jyotismita Chaki et al.
Nowadays, with the improvement in communication through social network services, a massive amount of data is being generated from user's perceptions, emotions, posts, comments, reactions, etc., and extracting significant information from those massive data, like sentiment, has become one of the comp...
Soumitra Saha, Partho Sarathi Sarker, Alam Al Saud, Swakkhar Shatabda et al.
Most. Fatematuz Zohora, Marzia Hoque Tania, M. Shamim Kaiser, Mufti Mahmud
Type II Diabetes (T2D) is one of the most common lifestyle diseases which is characterized by insulin resistance. Lack of insulin's proper working causes uncontrollable blood glucose rise in the body which leads to life taking situations. Therefore, early detection of T2D is imperative to save many ...
Kamrul Islam, Md. Manjur Ahmed, Kamal Z. Zamli
Data stream clustering plays an important role in data stream mining for knowledge extraction. Numerous researchers have recently studied density-based clustering algorithms due to their capability to generate arbitrarily shaped clusters. However, most of the algorithms are either fully offline, hyb...
Miftahul Jannat Mokarrama, Mohammad Shamsul Arefin
In this paper, we present a recommendation system named as RSF for farmers, which can recommend farmers most suitable crops to produce in different areas. The system first detects a user's location and works with different agro-ecological and agro-climatic data in upazila level to calculate similari...
Md. Alamin Talukder, Rakib Hossen, Md. Ashraf Uddin, Mohammed Nasir Uddin et al.
Abstract Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial for identifying and preventing unauthorized transactions. While credit card fraud incidents are relatively...
Dewan Md. Farid, Chowdhury Mofizur Rahman
In this paper, we propose a new approach for detecting novel class in data stream mining using decision tree classifier that can determine whether an unseen or new instance belongs to a novel class. Most existing data mining classifiers can not detect and classify the novel class instances in real-t...
Sajal Halder, Md. Samiullah, Young-Koo Lee