Browsing by Author "Islam, Md. Shamiul"

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Evaluation of Unani preparations by in vivo analgesic activity test and in vitro antioxidant activity test.
(East West University, 7/12/2012) Islam, Md. Shamiul
Since centuries unani system of medicine is practiced in this continent. A comparison of unani medicine with the allopathic could give reliable clinical evaluation. For the experiment male Swiss albino mice of 1-2 Weeks of age, Weighing between 20-25gm, were collected from international center for diarrheal disease and research, Bangladesh (ICDDRB).Sorobin has analgesic activity. Its effect in writhing inhibition (Sorobin 3.4±2.8) was more than (Diclofenac Na 7.2±3.7). Balarista has antioxidant activity which was found by free radical scavenging activity using DPPH with IC50 value of 7.596.
Performance enhancement of 5G Network using NB-IoT with LTE-M and Novel RASS Algorithm
(BRAC University, 2022-01) Sakib, Md. Nazmur; Islam, Md. Shamiul; Islam, Md. Rifatul; Zaman, Shakila; Shakil, Arif
With the fast growth of heterogeneous technology, today’s world is influenced by the Internet of Things (IoT) in immense ways. IoT networks connect resource constrained devices to provide automatic services which require low energy consump tion rates, less memory to store information, better bandwidth rate, high processing speed, and a wide range of coverage to ensure a good Quality of Service (QoS). Recently, energy consumption is becoming increasingly concerned for the large de gree of IoT devices. Therefore, NB-IoT and LTE-M, low-power wide-area networks standardized by 3GPP, are used in 5G technology to cope with the required power. Moreover, another significant concern in the IoT network is resource allocation that guarantees load balancing along with low operational cost and less energy consump tion. In this work, OEA algorithm is used by incorporating appropriate data and parameters for resource allocation in 5G enable NB-IoT and LTE-M networks. We have also proposed a novel algorithm-RASS with Probabilistic Mating, which is used to assign transmissions and powers in an efficient manner. RASS is enabled to generate resource allocation strategies to produce a new optimization model for solving issues faster.This procedure fits the computation requirements, unless that statement is fulfilled, the alteration process goes successfully. This is best explained using methods farther down.
Power Generarion, Transmission, Distribution and Protection System Equipments of Siddhirganj 2×120 MW Peaking Power Plant
(East West University, 4/15/2015) Islam, Md. Shamiul; Sarkar, Mithun; Asif, Abdullah Al
We did our internship at Siddhirganj 2×120 MW Peaking Power Plant located at Siddhirganj, Narayanganj on the bank of the river Shitalakkha from 23th August to 20th September 2014 and this internship report is the result of those 15 days attachment. Our duration of internship period was divided into four sections: generation, instrumentation and control (I&C), mechanical and electrical. During our internship period we gathered practical experiences over the topics related to power generation, switchgear protection and power distribution which we have learned inside the class room or from books. In this report we have focused on the processes which are used in Siddhirganj 2×120 MW Peaking Power Plant. For power generation, natural gas is used in Siddhirganj 2×120 MW Peaking Power Plant. With the help of the plant engineers we observed the control room, protective equipments such as relays, circuit breakers. We acquired knowledge about various types of transformers, isolators, circuit breakers, lightning arresters, current transformers, potential transformers and other equipments of the power station, the details of which are described in the text.
Real-Time Medical Image Classification with ML Framework and Dedicated CNN–LSTM Architecture
(Hindawi Publications, 2022) Salehin, Imrus; Islam, Md. Shamiul; Amin, Nazrul; Baten, Md. Abu; Noman, S. M.; Saifuzzaman, Mohd; Yazmyradov, Serdar
In the domain of modern deep learning and classification techniques, the convolutional neural network (CNN) stands out as a highly successful and preferred method for image classification in artificial intelligence. Especially in the medical field, CNN has proven to be an ideal approach for analyzing medical data and accurately identifying diseases. Over the recent years, CNN has demonstrated significant potential and success in various computer vision tasks, with medical image classification being one of the prominent applications. In our study, we introduce a novel custom CNN model called MedvCNN, designed for classifying different types of classes. We conduct experiments with various image sizes to explore their versatility. In addition, long short-term memory (LSTM), a type of recurrent neural network (RNN), is incorporated into our approach. LSTM is specifically tailored to handle sequential data, making it ideal for time series analysis. However, its capabilities extend beyond time series data and are effectively applied to various sequential data types, including sequential vectors derived from image data. One of the key advantages of utilizing LSTM for image classification is its ability to effectively memorize and capture important features in the image data. This feature is particularly advantageous in medical image processing, where precise and accurate identification of key attributes is crucial for successful diagnosis and analysis. Furthermore, our experiments reveal that the hybrid custom LSTM model, MedvLSTM, a RNN algorithm, surpasses other methods in the domain of medical image classification. Our study places significant emphasis on attaining robust classification performance for medical image data through a sophisticated, parameter free approach, complemented by an ablation study, and comprehensive statistical analysis. This comprehensive analysis and evaluation allow us to gain a deeper understanding of the model’s effectiveness and its potential impact in the field of medical image analysis. We compare these two approaches to a baseline CNN architecture, aiming to streamline the classification process, reduce time consumption, and improve cost efficiency. Additionally, we present a real-time web-based AutoML framework along with a practical demonstration. Ultimately, our research provides a thorough investigation of the current state-of-the-art in medical image analysis accuracy, focusing on the utilization of neural networks and LSTM.