Farming holds great importance in India, a country that depends significantly on agriculture. According on the 2022-23 census, the proportion of Gross Value Added (GVA) contributed by agricultural and associated secto...
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The U-Net architecture is the focus of this study, which optimizes biomedical picture segmentation. Improving performance in contexts with limited resources is the goal. The methodology uses GradCAM++, k-fold cross-va...
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The class of maximal-length cellular automata (CAs) has gained significant attention over the last few years due to the fact that it can generate cycles with the longest possible lengths. For every l of the form l = 2...
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This paper presents a concise methodology for the detection of partially reduplicated Multi-Word Expressions (MWEs) in Bengali texts. The entire process of identifying such reduplicated forms is carried out in two dis...
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A new stochastic coordinate descent deep learning architectures optimization is proposed for Automated Diabetic Retinopathy Detection and Classification from different data sets and convolution networks. Initially, th...
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This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in differe...
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This paper introduces a simple yet effective approach for developing fuzzy logic controllers(FLCs)to identify the maximum power point(MPP)and optimize the photovoltaic(PV)system to extract the maximum power in different environmental *** propose a robust FLC with low computational complexity by reducing the number of membership functions and *** optimize the performance of the FLC,metaheuristic algorithms are employed to determine the parameters of the *** evaluate the proposed FLC in various panel configurations under different environmental *** results indicate that the proposed FLC can easily adapt to various panel configurations and perform better than other benchmarks in terms of enhanced stability,responsiveness,and power transfer under various scenarios.
This Innovative Practice Category Full Paper presents the remote implementation of an embedded systems capstone project for computerengineering students. A capstone project is a feature of most undergraduate programs...
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ISBN:
(纸本)9798350351507
This Innovative Practice Category Full Paper presents the remote implementation of an embedded systems capstone project for computerengineering students. A capstone project is a feature of most undergraduate programs in computerengineering. Such a project is usually meant to expose students to the development of a large system from conceptualization to its final implementation, involving substantial design and development of hardware and software components. In our university, students were given the opportunity to work on a cutting-edge problem focused on healthcare - 'Designing a wearable device that automatically detects human activities'. It is an area where a large amount of research is ongoing, and hundreds of scientific papers are published every year. In addition, students had to evaluate and adopt techniques from existing literature and adapt them to meet the problem requirements. Students equipped themselves with state-of-the-art hardware, Bio-signal processing, machine learning, power optimization and secure communications to design the wearable. Thus, the project reinforced their knowledge of fundamentals, while exposing them to a problem with no obvious solution. Through the capstone project, students are able to better appreciate the relevance of the various components in the computerengineering curriculum to large-scale computerengineering projects. Students are organized into teams of six to execute the project. The COVID-19 pandemic resulted in the university migrating the teaching online for all courses. This was particularly challenging to implement for the capstone project as one of the key requirements is for the members to work together and subsystems to interact with each other. A course refresh resulted in a credits update and this provided a unique opportunity for the teaching team to re-design the project with core Industry 4.0 technologies such as hardware acceleration, remote processing, networking, remote analytics, and secure protoc
This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease ...
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This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease diagnosis has demonstrated commendable effectiveness in promptly diagnosing patients and curbing infection transmission. The study introduces a deep learning-based model tailored for COVID-19 detection, leveraging three prevalent medical imaging modalities: computed tomography (CT), chest X-ray (CXR), and Ultrasound. Various deep Transfer Learning Convolutional Neural Network-based (CNN) models have undergone assessment for each imaging modality. For each imaging modality, this study has selected the two most accurate models based on evaluation metrics such as accuracy and loss. Additionally, efforts have been made to prune unnecessary weights from these models to obtain more efficient and sparse models. By fusing these pruned models, enhanced performance has been achieved. The models have undergone rigorous training and testing using publicly available real-world medical datasets, focusing on classifying these datasets into three distinct categories: Normal, COVID-19 Pneumonia, and non-COVID-19 Pneumonia. The primary objective is to develop an optimized and swift model through strategies like Transfer Learning, Ensemble Learning, and reducing network complexity, making it easier for storage and transfer. The results of the trained network on test data exhibit promising outcomes. The accuracy of these models on the CT scan, X-ray, and ultrasound datasets stands at 99.4%, 98.9%, and 99.3%, respectively. Moreover, these models’ sizes have been substantially reduced and optimized by 51.93%, 38.00%, and 69.07%, respectively. This study proposes a computer-aided-coronavirus-detection system based on three standard medical imaging techniques. The intention is to assist radiologists in accurately and swiftly diagnosing the disease, especially during the screen
Electrocardiogram (ECG) signals are the most common tool to evaluate the heart’s function in cardiovascular diagnosis. Irregular heartbeats (arrhythmia) found in the ECG play an essential role in diagnosing cardiovas...
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In federated learning (FL), the communication constraint between the remote clients and the Parameter Server (PS) is a crucial bottleneck. For this reason, model updates must be compressed so as to minimize the loss i...
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