Anindya Das Antar, Masud Ahmed, Md Atiqur Rahman Ahad
Human Activity Recognition using embedded sensors has lately made renowned development and is drawing growing attention in numerous application domains including machine learning, pattern recognition, context awareness, and human-centric sensing. Due to the lacking of a prominent analysis of this to...
Akinul Islam Jony, Arjun Kumar Bose Arnob
The growth of Internet of Things (IoT) gadgets has ushered in a new era of connectedness and convenience, but it has also sparked worries about security flaws. Long Short-Term Memory (LSTM) networks are used in this research's use of intrusion detection as a novel strategy to strengthen IoT security...
Sohrab Hossain, Ahmed Abtahee, Imran Kashem, Mohammed Moshiul Hoque et al.
A crime is an action which constitutes a punishable offence by law. It is harmful for society so as to prevent the criminal activity, it is important to understand crime. Data driven researches are useful to prevent and solve crime. Recent research shows that 50% of the crimes are committed by only ...
Zakia Ferdousi, Akira Maeda
Fraud detection is of great importance to financial institutions. This paper is concerned with the problem of finding outliers in time series financial data using Peer Group Analysis (PGA), which is an unsupervised technique for fraud detection. The objective of PGA is to characterize the expected p...
Nuruzzaman Faruqui, Mohammad Abu Yousuf, Md Whaiduzzaman, AKM Azad et al.
The Internet of Medical Things (IoMT) has become an attractive playground to cybercriminals because of its market worth and rapid growth. These devices have limited computational capabilities, which ensure minimum power absorption. Moreover, the manufacturers use simplified architecture to offer a c...
Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, Anubhav Sarkar et al.
Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, Anubhav Sarkar, S.M Towhidul Islam Tonmoy, Aman Chadha, Amit Sheth, Amitava Das. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
Dewan Md. Farid, Mohammad Zahidur Rahman
Abstract—Recently, research on intrusion detection in computer systems has received much attention to the computational intelligence society. Many intelligence learning algorithms applied to the huge volume of complex and dynamic dataset for the construction of efficient intrusion detection systems ...
Asu Kumar Singh, Anupam Kumar, Mufti Mahmud, M. Shamim Kaiser et al.
A novel strain of Coronavirus, identified as the Severe Acute Respiratory Syndrome-2 (SARS-CoV-2), outbroke in December 2019 causing the novel Corona Virus Disease (COVID-19). Since its emergence, the virus has spread rapidly and has been declared a global pandemic. As of the end of January 2021, th...
Dezhi Han, Hongxu Zhou, Tien‐Hsiung Weng, Zhongdai Wu et al.
Rony Chowdhury Ripan, Iqbal H. Sarker, Syed Md. Minhaz Hossain, Md Musfique Anwar et al.
Md. Abdul Awal, Mehedi Masud, Md. Shahadat Hossain, Abdullah Al-Mamun Bulbul et al.
The whole world faces a pandemic situation due to the deadly virus, namely COVID-19. It takes considerable time to get the virus well-matured to be traced, and during this time, it may be transmitted among other people. To get rid of this unexpected situation, quick identification of COVID-19 patien...
Raihan Ul Islam, Mohammad Shahadat Hossain, Karl Andersson
It is an era of Internet of Things, where various types of sensors, especially wireless, are widely used to collect huge amount of data to feed various systems such as surveillance, environmental monitoring, and disaster management. In these systems, wireless sensors are deployed to make decisions o...
Amanullah Asraf, Md. Zabirul Islam, Md. Rezwanul Haque, Md. Milon Islam
During this global pandemic, researchers around the world are trying to find out innovative technology for a smart healthcare system to combat coronavirus. The evidence of deep learning applications on the past epidemic inspires the experts by giving a new direction to control this outbreak. The aim...
Qiao Liu, Hui Xue
Unsupervised domain adaptation (UDA) has been received increasing attention since it does not require labels in target domain. Most existing UDA methods learn domain-invariant features by minimizing discrepancy distance computed by a certain metric between domains. However, these discrepancy-based m...
Shakil Ahmed Sumon, Mohammad Raihan Goni, Niyaz Bin Hashem, Md Tanzil Shahria et al.
In this paper, we have explored different strategies to find out the saliency of the features from different pretrained models in detecting violence in videos. A dataset has been created which consists of violent and non-violent videos of different settings. Three ImageNet models; VGG16, VGG19, ResN...