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An Improved Ultra High Gain Quadratic Boost Converter with Classical Voltage Doubler Circuit 3

An Improved Ultra High Gain Quadratic Boost Converter with C...

引用

3rd International Conference on Power, Control and Computing Technologies, ICPC2T 2024

作者： Al Mahbub Emon, Abdllah Sultana, Shariya Ritu, Faiza Razzaque Leading University Electrical and Electronic Engineering Sylhet Bangladesh Leading University Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350349207

In this paper, a modified ultra-high gain DC-DC boost converter has been proposed. The proposed converter has improved the performance of the conventional converter by increasing the voltage gain 2.34 times that of the conventional converter by adding a classical doubler circuit (2 capacitors and 2 diodes) in the design structure. In addition, the continuous input current features are retained in the proposed structure. The steady-state modeling, including the operating principles and the stress calculation of the components, has been illustrated. The proposed model has been simulated in Matlab/Simulink software with proper component specification. The simulation's results validate the correctness of the proposed converter by aligning well with the findings of the mathematical computation. © 2024 IEEE.

关键词： Boost converter

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Drug Addiction Analysis in Bangladesh Using Machine Learning 15

Drug Addiction Analysis in Bangladesh Using Machine Learning

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Ahmed, Faruk Ahmed, Ferdous Mubin, Arif Shahriar Rahman, Habibur Ayon, Rokonozzaman Daffodil International University Department of Computer Science & Engineering Dhaka Bangladesh Leading University Department of Computer Science & Engineering Sylhet Bangladesh

ISBN: (纸本)9798350370249

The widespread problem of drug addiction remains a major public health issue in Bangladesh, demanding a thorough exploration of its root causes and predictive models. Data was gathered from 1104 individuals' survey responses, covering diverse demographic and behavioral characteristics. To evaluate their predictive potential, the data was examined using machine learning methods such as Decision Tree, Logistic Regression, K-Nearest Neighbor (KNN) Classifier, Naïve Bayes Classifier, and XGBoost Classifier. Our results demonstrate that the machine learning models serve as effective tools for comprehending and forecasting drug addiction within the Bangladeshi context. The performance metrics for each algorithm are as follows: Decision Tree (98% accuracy), KNN (98% accuracy), Logistic Regression (92% accuracy), Naïve Bayes (88% accuracy), and XGBoost (98%). These models exhibit promising capabilities, with Decision Tree, KNN, and XGBoost notably achieving a 98% accuracy rate in predicting drug addiction. These findings establish a solid foundation for future investigations into the complexities of drug addiction and the creation of more sophisticated predictive models specifically tailored to the context of Bangladesh. © 2024 IEEE.

关键词： Logistic regression

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Assessing the Robustness of Machine Learning Algorithms for Cardiovascular Disease Detection Across Diverse Clinical Datasets 1

Assessing the Robustness of Machine Learning Algorithms for ...

引用

1st International Conference on Advanced engineering and Technologies, ICONNIC 2023

作者： Proma, Moulia Das Akther, Khadiza Priya, Bushra Siddika Rahaman, Md. Jamaner Kohinoor, Md. Saidur Rahman Leading University Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350306484

Cardiovascular diseases (CVD) continue to pose significant health risks globally, accentuating the need for early and precise detection mechanisms. With the evolution of computational methods in healthcare, machine learning offers transformative solutions for diagnostic accuracy. This research aims to identify an algorithm with consistent performance across multiple datasets for potential integration into a cardiac disease prediction platform. We examined nine prominent machine learning algorithms, namely Support Vector Machine (SVM), Gradient Boosting (GB), Random Forest (RF), Logis-tic Regression (LR), Decision Tree (DT), K-Nearest Neigh-bor (KNN), Naive Bayes (NB), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP), and evalu-ated their predictive performance across two heterogeneous datasets. Both datasets encompass 14 attributes but differ in instance sizes: 303 and 1025, respectively. Through a meticulous methodological framework, the data underwent preprocessing, splitting, and model training, followed by validation using metrics such as Precision, Recall, F1 score, and Accuracy, coupled with a confusion matrix for detailed class-based evaluation. Our findings revealed that the Random Forest and MLP algorithms exhibited superior consistency and robustness in disease prediction across both datasets, achieving a peak accuracy of 95.14%. While XGBoost performed proficiently on one dataset, its performance wavered in a cross-dataset scenario. Based on these findings, either the Random Forest or Multilayer Perceptron models are recommended for developing a robust heart disease prediction system. This research not only affirms the potential of machine learning in revolutionizing CVD diagnostics but also underscores the importance of algorithm selection based on dataset characteristics. © 2023 IEEE.

关键词： Forecasting

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Efficient Segmentation Techniques for the Automated Detection of Acute Lymphoblastic Leukemia (ALL) in Microscopic Images 26

Efficient Segmentation Techniques for the Automated Detectio...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Sharma, Nabajyoti Toma, Anupoma Angasree Saidur Rahman Kohinoor, Md. Leading University Computer Science & Engineering Sylhet Bangladesh

ISBN: (纸本)9798350359015

Acute Lymphoblastic Leukemia (ALL), a significant hematologic malignancy impacting both children and adults, demands accurate and efficient diagnostic approaches. Current practices involve manual White Blood Cell (WBC) counting from microscopic images, which is not only cumbersome but also susceptible to human errors. Addressing this diagnostic bottleneck, the authors aim to develop a computational model for the automated detection of ALL from blood smear images. As such, with a focus on refining image pre-processing strategies through image grayscaling, filtering or enhancement and noise removal techniques, this study underscores the efficacy of different segmentation methods in discerning leukemia cells. Using the ALL-IDB dataset, a repository of leukemia microscopic images, we evaluated three segmentation models: Watershed Based Method, Otsu's Binarization Method, and the Canny Edge Detector. Our comprehensive analysis, founded on key performance metrics, reveals Watershed as the most promising with an accuracy rate of 91.09%, while the others demonstrated 90.95% and 85.11% respectively. Rooted in state-of-the-art segmentation techniques, this research has the potential to significantly streamline the diagnostic landscape for ALL, making it more accurate, efficient, and less error-prone. © 2023 IEEE.

关键词： Acute Lymphoblastic Leukemia (ALL) Canny Edge Detector Image Segmentation Otus's Binarization Watershed White Blood Cell

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Enhancing Poultry Farm Productivity Using IoT-Based Smart Farming Automation System 26

Enhancing Poultry Farm Productivity Using IoT-Based Smart Fa...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Rahman, Mahbubur Kohinoor, Md Saidur Rahman Sami, Aftar Ahmad Leading University Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350359015

The swift expansion of smart farming is mainly attributable to the industry's noteworthy impact of the Internet of Things (IoT) and various sensors have revolutionized data collection across different farming aspects, encompassing machine performance and supply chain operations. Addressing this technological evolution, our paper introduces a comprehensive smart farming automation system, apt for multiple agricultural domains. Its primary focus is to bolster farm productivity through precise environmental monitoring and built-in analytics to interpret data trends over time, offering actionable insights and recommendations for optimal production growth. To illustrate its practical application, we specifically delve into its implementation in poultry farming, emphasizing sensors that track temperature and humidity, which are paramount for poultry well-being. Integrated features include Wi-Fi-based real-time monitoring, automation for food and water distribution, rain-protective curtain controls, and a dedicated mobile application paired with a web server, utilizing data scrapping APIs to discern and suggest optimal farming patterns based on accumulated and real-time data. This system also offers precise tracking of environmental factors and an alert mechanism, leveraging IR sensors, to signal food storage deficits and extreme conditions. As such, this project presents itself as a feasible alternative to traditional farming practices, which typically pose environmental challenges and crave considerable labor through data-driven feedback and streamlined automation. © 2023 IEEE.

关键词： agricultural automation Agriculture data analysis farm management IoT sensors smart poultry farming

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Performance Evaluation of Deep Learning-Based Facial Recognition Models on Mobile Computing Environments 11

Performance Evaluation of Deep Learning-Based Facial Recogni...

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11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Munim, Md Abdul Rahman Kohinoor, Md Saidur Leading University Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350326147

Over the past few years, facial recognition technology has become an essential element in a range of applications, such as security systems, user verification, a nd s ocial media networks. The deployment of face recognition models on low-resource devices, such as low-end smartphones, poses considerable challenges owing to the restricted processing power and memory capacity at hand. The objective of this research is to determine the optimal deep learning-driven facial recognition algorithm that can provide prompt results in resource-constrained environments. The study conducted a comparative analysis of four widely used models, namely FaceNet, OpenFace, DeepFace, and VGGFace, with respect to their performance on the LFW (Labeled Faces in the Wild) dataset, which comprises a range of facial images. The assessment criteria comprised of accuracy, recall, F1 score, inference time, and memory utilization. The study employed an extensive codebase to preprocess and generate facial embeddings for each model, assess their efficacy, and present the findings in a visual format. The results of t he study indicate that FaceNet exhibited superior performance compared to the other models, achieving an accuracy rate of 94.04 %. Additionally, FaceNet demonstrated the shortest inference time of 0.0181 and the lowest memory usage. The exceptional proficiency exhibited by FaceNet renders it a highly suitable option for conducting facial recognition in realtime on devices with limited resources. The present investigation underscores the significance of carefully choosing suitable facial recognition models that are tailored to specific c ontexts a nd l ays t he g roundwork for forthcoming studies aimed at enhancing efficacy i n restricted settings. © 2023 IEEE.

关键词： Face recognition

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A Comparative Study on Different Machine Learning Techniques in Diabetes Risk Assessment 3

A Comparative Study on Different Machine Learning Techniques...

引用

3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Akther, Mahnur Chowdhury, Zahara Rahman Tabassum, Anika Kohinoor, Md. Saidur Rahman Leading University Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350338607

Diabetes is a chronic disease in which the body's ability to process glucose is impaired due to insufficient insulin production or utilization. This can result in high blood sugar levels and other health issues. Since the majority of our country's population are not conscious about their lifestyle and are unaware of the complications and casualties that diabetes can lead to if it is not examined timely, assessing their risk for diabetes is an important step in prevention and early detection. Assessing one's diabetes risk prior to medical diagnosis is crucial to enable timely intervention and management of the disease. Our paper intends to provide such a solution that would help detect the disease risk beforehand. We selected two diabetes datasets: PIMA Indian and Sylhet dataset for evaluating ten different models, namely-Support Vector Machine, Random Forest, Naive Bayes, Decision Tree, K Nearest Neighbor, Logistic Regression, Adaboost, Gradient Boost, XGBoost, and Multilayer perceptron. We applied Grid Search and Stratified K-fold cross-validation to asses model's performance. Our goal is to determine the best-performing model for predicting diabetes. From the analysis, Random Forest outperformed in PIMA Indian Dataset with 84.21% accuracy and Gradient Boost outperformed in Sylhet dataset with 98.85% accuracy. So, for the two different featured datasets Random Forest and Gradient Boost scored highest as the best-predicting models. © 2023 IEEE.

关键词： Forecasting

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Performance Analysis for Convolutional Neural Network Architectures using Brain Tumor Datasets: A Proposed System

Performance Analysis for Convolutional Neural Network Archit...

引用

2023 International Workshop on Artificial Intelligence and Image Processing, IWAIIP 2023

作者： Chowdhury, Md. Jalal Uddin Kibria, Shafkat Leading University Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350382914

Brain tumors remain a pressing global health concern, with high mortality rates despite significant medical advancements. The brain tumor is a potentially deadly condition, and its categorization creates an enormous challenge for radiologists due to the diverse composition of tumor cells. In recent times, there has been a growing interest in the development of computer-aided diagnostic systems that use magnetic resonance imaging (MRI) to help in the identification of brain tumors. These systems have the potential to provide valuable support in the medical field. In this paper, we have proposed an experimental method for the extraction of brain tumors from 2D Magnetic Resonance Imaging (MRI) scans using a convolutional neural network (CNN). The qualitative study was conducted using a real-time online dataset that included a wide range of tumor sizes, locations, forms, and varying image intensities. The proposed methodology aims to deliver the most effective model capable of distinguishing between normal brain structures and various types of brain tumors, such as glioma, meningioma, and pituitary tumors, using these MRI images. The architecture of the network, the choice of hyperparameters, and the nuances of the optimization algorithms - all these factors play a crucial role in determining the model's performance. In our work, CNN achieved an accuracy rate of 98.91%, which is quite impressive. In addition, results from experiments demonstrated that the proposed method is efficient and suitable for computer-assisted brain tumor detection. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Integrating PRINCE2 and Agile Methodologies in Software Development: Unveiling Opportunities and Addressing Challenges 5

Integrating PRINCE2 and Agile Methodologies in Software Deve...

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5th International Conference on Electrical, Communication and computer engineering, ICECCE 2024

作者： Rahman, Md Shahinur Ahmed, Emon Leading University Computer Science and Engineering Sylhet Bangladesh Edinburgh Napier University United Kingdom

ISBN: (纸本)9798331529437

Modern software development now necessitates combining various project management approaches to tackle the fast speed of technical advancements and intricate project demands. This study examines the possibilities and obstacles of integration. Utilising PRINCE2 and Agile methodologies in software development projects. The research emphasises PRINCE2's strong governance and structured procedures, as well as Agile's adaptability and reactivity to change, especially in cultural contexts. Combining PRINCE2's control mechanisms with Agile's flexibility can improve project management by solving cultural difficulties found in agile transitions. © 2024 IEEE.

关键词： Project management

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Deep Learning-Based Local Fish Classification: A Comparative Study of VGG16 Models and Multiple Classifiers 11

Deep Learning-Based Local Fish Classification: A Comparative...

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11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Hossain Shozib, Kazi Shahadat Rahman Kohinoor, Md. Saidur Leading University Department of Computer Science and Engineering Sylhet Bangladesh

ISBN: (纸本)9798350326147

The classification of local fish species is a crucial task in many countries like Bangladesh, where fish serves as a major source of protein for the population. However, the lack of knowledge among the younger generation of Bangladeshi people about the different types of freshwater fish often leads to reliance on manual classification, leaving them susceptible to deceitful practices. But in the last few years, deep learning techniques have emerged as a promising solution to automate the classification process using high-level feature extraction from raw images. To address the mentioned problem, we need this kind of model which can be used in a limited resource setting with higher accuracy. In this paper, we present a comparative analysis of the VGG16 pretrained model with different classifiers, including SoftMax, Random Forest, and MLP classifiers, for the classification of 10 distinct local fish species in Bangladesh. Our custom dataset consisting of 2300 images of local fish species was used to train and evaluate the performance of these models. Our experimental results demonstrate that the SoftMax classifier achieves the highest accuracy of 95%, followed by the MLP classifier with an accuracy of 94 %, and the Random Forest classifier with an accuracy of 86%. We also found that the training time of the SoftMax classifier is significantly higher than the other two classifiers, while the Random Forest classifier has the lowest training time, albeit with relatively lower accuracy. Our findings provide valuable insights for researchers and practitioners to choose the appropriate classifier for local fish classification tasks, based on their requirements and constraints. © 2023 IEEE.

关键词： Image classification

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