Browsing by Author "Islam, Md Rashidul"

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    A deep dive into node-level analysis with fusion RNN model for smart LTE network monitoring
    (BRAC University, 2023-09) Islam, Md Rashidul; Alam, Golam Rabiul
    Predicting and understanding traffic patterns have become important objectives for maintaining the Quality of Service (QoS) standard in network management. This change stems from analyzing the data usage on cellular internet networks. Cellular network optimiser frequently employ a variety of data traffic prediction algorithms for this reason. Traditional traffic projections are often made at the high-level or generously large regional cluster level and therefore has the lacking in precised forecation. Furthermore, it is difficult to obtain information on eNodeB-level utilisation with regard to traffic predictions. As a result, using the conventional approach causes user experience degradation or unnecessary network expansion. Developing a traffic forecasting model with the aid of multivariate feature inputs and deep learning techniques was one of the objective of this research. It deals with extensive 6.2 million real network time series LTE data traffic and other associated characteristics, including eNodeB-wise PRB utilisation. A cutting-edge fusion model based on Deep Learning algorithms is suggested. Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU) are three deep learning algorithms that when combined allow for eNodeB-level traffic forecasting and eNodeB-wise anticipated PRB utilisation.The proposed fusion model’s R2 score is 0.8034, outperforms the conventional state-if-the-art models. This study also proposed a unique method that thoroughly examines individual nodes for the Smart Network Monitor. This approach follows adjustments made to soft capacity parameters at the eNodeB level, aiming for immediate improvement or long-term network growth to meet a consistent QoS standard. The algorithm relies on expected PRB utilization.
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    Design, Simulation, and Implementation of a Smart Saline Infusion Control and Monitoring System using Microcontroller and IoT
    (Bangladesh Electronics and Informatics Society, 2021-11-27) Islam, Md Rashidul; Islam, Md. Rafikul Islam; Uddin, Sarif; Hosen, Kabir; Bhuyan, Muhibul Haque
    Since Bangladesh is the most densely populated country in the world, we need to develop smart and automatic devices to serve our people better. At present, in hospitals and clinics, saline is being infused manually and without any feedback system. As a result, sometimes it causes overflow or underflow incidents and hence creates many problems for the patients. To protect the patients from such accidents, we proposed, designed, simulated, and implemented a prototype smart saline infusion control and monitoring system based on an Arduino microcontroller and the Internet of Things (IoT). Such a device would replace the need for a Nurse to constantly keep an eye on the saline bottle level connected to a patient. If the nurse ever fails to control the saline flow at an appropriate rate, the system will automatically adjust the flow rate. When the bottle gets empty, the fluid flows back into the saline bottle, and it may harm the patient. However, our system is IoT based, and this can monitor the saline level of the bottle continuously and update it in the database, and generates alert signals when the saline level goes below a threshold level. If the control room fails to respond, a screw-actuated clamp mechanism will stop the flow and save the patient. In the future, some features will be added to this device so that it can help our patients, doctors, and nurses. Besides, other health monitoring parameters may be added with this device and a central database system can be made for future reference. It will also help the government offices and insurance companies to get the actual health data of a person.