https://ar.iub.edu.bd/handle/11348/21 2026-04-09T14:15:22Z https://ar.iub.edu.bd/handle/11348/1067 Design and Implementation of a Microstrip Patch Antenna–Based Sensing System for pH Estimation of Aqueous Solutions Using Machine Learning Nirob, Nafish Kabir; Islam, Md. Mishurul This paper presents the design and implementation of a microstrip patch antenna-based sensing system for pH estimation of aqueous solutions using machine learning. Conventional pH sensors are widely used, such as frequent calibration, easy contamination, the measurement range is limited, and the maintenance is expensive. In order to overcome these limitations, the proposed system uses rectangular microstrip patch antenna fabricated using FR4 substrate for the detection of change in the dielectric properties of liquid samples. Variations in resonant frequency, return loss (S11) and bandwidth are then analyzed to provide the predict pH level. Experimental measurements were conducted for solutions with a pH value ranging from 4 to 12 by using a Vector Network Analyzer (VNA) with the results verified by simulation results of CST Studio Suite. Furthermore, Random Forest, Support Vector Regression (SVR) and kNearest Neighbors (kNN) machine learning models were also trained with the collected dataset in order to predict the pH with respect to the antenna response parameters. Out of them, Random Forest showed the highest accuracy (𝑅 2 level of higher than 0.85) with the lowest MAE (Mean Absolute Error) showing also the strong predictive reliability. The obtained results validate the concept that a low-cost, wide-area and contact-less pH monitoring solution is obtained by the combination of microwave sensing and data-driven modeling. The proposed system has potential applications in environmental monitoring, industrial process control, fish farming and agricultural water management especially for resource constrained environments like that of Bangladesh. 2025-01-01T00:00:00Z https://ar.iub.edu.bd/handle/11348/1041 A Machine Learning Approach for Multi-Level Anxiety Screening among University-Going Students using Wireless EEG Signals Sakib, Nazmus; Islam, Md Kafiul; Faruk, Tasnuva Anxiety is a widespread mental health condition affecting millions globally, often resulting in significant emotional and physical symptoms. Accurate detection of anxiety levels is essential to provide timely interventions and prevent severe complications. This study explores a machine learning-based approach for multilevel anxiety classification among young adults using EEG signals. The GAD-7 screening tool was used to assess and categorize participants into different anxiety severity groups. EEG data was then recorded, processed, and segmented into 1, 3, and 5 second segments to evaluate the impact of segment duration on classification accuracy. Four channel combinations were tested for comparisons in performance. Feature extraction included eleven time and frequency domain features. The Bagged Trees classifier was applied to classify anxiety levels based on these features. The findings of this work show the potential of EEG-based systems as non-invasive tools for anxiety screening that could support more precise mental health diagnostics. Conference Paper 2025-06-23T00:00:00Z https://ar.iub.edu.bd/handle/11348/1040 EEG-based Mouse Cursor Control using Motor Imagery-based Brain-Computer Interface Rafique, Sayem Bin; Roja, Saima Tasfia; Rhaman, Md. Asikur A brain-computer interface (BCI) framework uses computer algorithms to detect mental activity patterns and manipulate external devices. Most commonly used in imaging technologies is electroencephalography (EEG) because of its non-invasiveness. The evaluation method used in assessing the output of an EEG-based BCI system is classifying EEG signals for particular applications. In this study, we present a system of EEG-based mouse cursor control using a Motor Imagery-based Brain-Computer Interface (MI-BCI). The growth of technology and artificial intelligence inspired us to develop a system for physically impaired individuals as well as to work with electroencephalogram (EEG) signals. This signal is a noninvasive and low-cost method to extract brain signals from a subject. Our work also includes the EEG signal acquisition as well as advanced signal processing methods to utilize the MI-BCI-based brain activity. This work also includes the machine learning algorithm which carried out the system to do the successful cursor movement using binary classification. Furthermore, the successful mouse cursor movement added up the higher accuracy of 93.83% which is the result of the offline dataset. 2023-10-01T00:00:00Z https://ar.iub.edu.bd/handle/11348/949 EEG-based Mouse Cursor Control using Motor Imagery Brain-Computer Interface Roja, Saima Tasfia; Bin Rafique, Sayem; Rhaman, Md. Asikur; Sakib, Nazmus; Islam, Md Kafiul Brain-computer interface (BCI) is a system that collects, analyzes, and transforms brain signals into commands. The brain experiences repetitive, oscillatory electrical changes caused by these activities that have a very low voltage of only a few microvolts (µV).The term electroencephalogram (EEG) refers to the non-invasive recording of this electrical activity from the scalp. The signals are then analyzed in a computer to identify the desired action after signal acquisition. Relevant features are gathered and translated into commands that operate an output device or carry out the command. The user is subsequently provided with feedback to confirm that the command has been carried out correctly This work focuses on the development and implementation of a Mouse Cursor Control system using Motor Imagery (MI) BCI, using data recorded with the Emotiv EPOC+ headset and processed using our algorithm in the MATLAB software. While similar works do exist, most tend to focus on one or more aspects of data processing such as classification. We acquired the data from subjects ourselves, and after processing the data using our algorithm, the system was implemented, and the cursor was moved. This makes our system a semi-online system, as opposed to offline systems. The only limitation of our system is that the system is implemented in semi-real time. Furthermore, accuracy was tested for different frequency bands and the highest accuracy of 93.60% was achieved using the offline dataset. 2024-05-03T00:00:00Z