Browsing by Author "Islam, Md Nahidul"

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A Hybrid Environment Control System Combining EMG and SSVEP Signal Based on Brain-computer Interface Technology
(SN Applied Sciences, 2021-08-23) Rashid, Mamunur; Bari, Bifta Sama; Sulaiman, Norizam; Mustafa, Mahfuzah; Hasan, Md Jahid; Islam, Md Nahidul; Naziullah, Shekh
The patients who are impaired with neurodegenerative disorders cannot command their muscles through the neural pathways. These patients are given an alternative from their neural path through Brain-Computer Interface (BCI) systems, which are the explicit use of brain impulses without any need for a computer's vocal muscle. Nowadays, the steady-state visual evoked potential (SSVEP) modality offers a robust communication pathway to introduce a non-invasive BCI. There are some crucial constituents, including window length of SSVEP response, the number of electrodes in the acquisition device and system accuracy, which are the critical performance components in any BCI system based on SSVEP signal. In this study, a real-time hybrid BCI system consists of SSVEP and EMG has been proposed for the environmental control system. The feature in terms of the common spatial pattern (CSP) has been extracted from four classes of SSVEP response, and extracted feature has been classified using K-nearest neighbors (k-NN) based classification algorithm. The obtained classification accuracy of eight participants was 97.41%. Finally, a control mechanism that aims to apply for the environmental control system has also been developed. The proposed system can identify 18 commands (i.e., 16 control commands using SSVEP and two commands using EMG). This result represents very encouraging performance to handle real-time SSVEP based BCI system consists of a small number of electrodes. The proposed framework can offer a convenient user interface and a reliable control method for realistic BCI technology.
Analyzing Marketing Mix Strategy of Bata Bangladesh
(Daffodil International University, 2024-05-19) Islam, Md Nahidul
In this report, Bata Bangladesh Ltd., a main shoes store with over 80 years of presence in Bangladesh, has crafted a hit marketing blend that aligns with its goal segments, encompasses strategic distribution channels, and employs a numerous array of promotional activities. This complete approach has enabled Bata to efficaciously attain its target market, decorate logo notion, and hold a dominant role inside the Bangladeshi shoes market. Bata's pricing approach is carefully tailored to cater to its various customer segments. Mass marketplace products, designed for ordinary wear and attractive to a huge consumer base, are priced decrease than mid-marketplace and top class marketplace products. This tiered pricing approach ensures that Bata caters to a huge variety of choices and budgets, permitting it to capture a tremendous proportion of the market.
Classification of EEG-Based Auditory Evoked Potentials Using Entropy-Based Features and Machine Learning Techniques
(IEEE, 2023-11-06) Islam, Thamina; Ahmed, Firoz; Ahmed, Nayem; Naziullah, Shekh; Islam, Md Nahidul; Rashid, Mamunur
Hearing loss is a prevalent impairment that disrupts interactions with others and individuals' learning abilities. Immediate and accurate diagnosis of hearing loss using Electroencephalogram (EEG) signals, particularly Auditory Evoked Potentials (AEP), is considered the most effective approach to address this issue. The AEP signals, generated in the cerebral cortex in response to auditory stimuli, serve as the most reliable method for diagnosing deafness. This study introduces a novel approach for detecting hearing ability through the classification of EEG-AEP signals. The current experiment makes use of a publicly available dataset that contains AEP responses from 16 people who responded to auditory stimuli on either the left or right side. Sample Entropy is employed to extract the feature, capturing the complex temporal dynamics of the EEG signals. Four popular machine learning-based classifiers, namely Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Logistic Regression (LR), are utilized for classification purposes. The results indicate that SVM achieves the highest classification accuracy of 99.37% with subject-4 and the average accuracy of 90.74% is achieved with all subjects. This finding shows the effectiveness of Sample Entropy as a feature extraction technique for characterizing AEPs and highlights the potential of SVM as a robust classifier for the accurate identification of auditory stimuli localization. The accuracy achieved in this study indicates a promising direction for the development of reliable and non-invasive methods for hearing-related diagnoses.
Evaly Business Model Inspection
(Daffodil International University, 2020-12-28) Islam, Md Nahidul
The covid-19 virus has changed the whole world. The personal life of people, as well as the world economy, changed dramatically because of covid-19. Businesses, no matter small or big, are adjusting with new regular techniques which covid-19 brought. According to, World business forum, at least 45% of small businesses will shut down due to coronavirus impact. Bangladesh's startup community faces the worst because of the nature of our business and the business environment. In this project, we Interviewed many startups, especially tech businesses. We focused on the impact they are facing due to covid-19 and the strategies they are using for surviving this invisible war. After carefully analyzing the situation, we recommended policies/strategies needed for the startup, which will help the entrepreneurs who are fighting hard. It’s not like that every business is having a hard time; many innovative companies are flourishing during this time also.
Lemon leaf chemical feature extraction over categorical images using Neural Network
(Daffodil International University, 2024-07-14) Islam, Md Nahidul
This paper convolutional neural networks (CNNs) have shown great promise for the categorization and chemical feature extraction of lemon leaves, among other agricultural applications. This paper investigates the use of CNNs, namely MobileNet, which achieves a maximum accuracy of 84%, for the precise age classification and chemical composition prediction of lemon leaves. In order to discern between the oldest, middle-aged, and young age groups and to forecast nutrient levels like potassium, calcium, magnesium, phosphate, and sulfate, the research uses deep learning algorithms to analyze leaf photos and extract complex information. Preprocessing leaf pictures, training MobileNet on an extensive dataset, and assessing model performance using measures like as accuracy, precision, recall, and F1-score are all part of the technique. The outcomes show how well MobileNet works to achieve high accuracy in tasks involving both chemical feature prediction and classification. By giving farmers strong tools to track leaf health, improve nutrient management plans, and increase crop output in a sustainable way, this work advances precision agriculture. To further improve the scalability and usefulness of CNN-based techniques in agricultural contexts, future research areas include expanding the dataset, investigating ensemble learning strategies, and incorporating real-time applications.
Quizlytics: Unlock Knowledge with AI-Powered Interactive Quizzes
(Daffodil International University, 2025-01-12) Islam, Md Nahidul
Quizlytics is an innovative platform that enhances education through engaging, AI-powered quizzes. The purpose of this project is to provide users with an interactive and enjoyable quiz that is customized to meet their needs. Key features include customizable quiz op- tions, various categories, timed assessments, and a comprehensive quiz history that allows users to track their progress over time. After completing a quiz, users receive immediate feedback, which includes detailed performance summaries and correct answers. This helps users better understand the material. The platform also promotes sharing with others, thus building a sense of community and connection between users. In addition, Quizlyt- ics includes a blog section with informative articles, a continuous improvement feedback system, and tools for educators to create and manage quizzes. Advanced features, such as AI-generated quizzes based on articles and visual displays of results, further enhance the user experience. In general, Quizlytics serves as a valuable tool for self-assessment and promotes ongoing learning and improvement through its diverse and engaging features.