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Data Driven Classification Of Opioid Patients Using Machine Learning - An Investigation, Saddam Al Amin, Md Saddam Hossain Mukta, Md Sezan Mahmud Saikat, Md Ismail Hossain, Md Adnanul Islam, Mohiuddin Ahmed, Sami Azam
Data Driven Classification Of Opioid Patients Using Machine Learning - An Investigation, Saddam Al Amin, Md Saddam Hossain Mukta, Md Sezan Mahmud Saikat, Md Ismail Hossain, Md Adnanul Islam, Mohiuddin Ahmed, Sami Azam
Research outputs 2022 to 2026
The opioid crisis has led to an increased number of drug overdoses in recent years. Several approaches have been established to predict opioid prescription by health practitioners. However, due to the complex nature of the problem, the accuracy of such methods is not yet satisfactory. Dependable and reliable classification of opioid dependent patients from well-grounded data sources is essential. Majority of the previous studies do not focus on the users’ mental health association for opioid intake classification. These studies do not also employ the latest deep learning based techniques such as attention and knowledge distillation mechanism to find better insights. …
Edge-Iiotset: A New Comprehensive Realistic Cyber Security Dataset Of Iot And Iiot Applications For Centralized And Federated Learning, Mohamed A. Ferrag, Othmane Friha, Djallel Hamouda, Leandros Maglaras, Helge Janicke
Edge-Iiotset: A New Comprehensive Realistic Cyber Security Dataset Of Iot And Iiot Applications For Centralized And Federated Learning, Mohamed A. Ferrag, Othmane Friha, Djallel Hamouda, Leandros Maglaras, Helge Janicke
Research outputs 2022 to 2026
In this paper, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the dataset has been generated using a purpose-built IoT/IIoT testbed with a large representative set of devices, sensors, protocols and cloud/edge configurations. The IoT data are generated from various IoT devices (more than 10 types) such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame …
Classification Of Skin Disease Using Deep Learning Neural Networks With Mobilenet V2 And Lstm, Parvathaneni N. Srinivasu, Jalluri G. Siva Sai, Muhammad F. Ijaz, Akash K. Bhoi, Wonjoon Kim, James J. Kang
Classification Of Skin Disease Using Deep Learning Neural Networks With Mobilenet V2 And Lstm, Parvathaneni N. Srinivasu, Jalluri G. Siva Sai, Muhammad F. Ijaz, Akash K. Bhoi, Wonjoon Kim, James J. Kang
Research outputs 2014 to 2021
Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning-based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), …