In the realm of neuroscience research, understanding and analyzing the three-dimensional structure of neurons is essential for unveiling brain functions and mechanisms, and technological advancements, particularly in ...
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ISBN:
(纸本)9798350386783;9798350386776
In the realm of neuroscience research, understanding and analyzing the three-dimensional structure of neurons is essential for unveiling brain functions and mechanisms, and technological advancements, particularly in high-resolution imaging, have significantly enhanced our ability to create detailed three-dimensional digital models of these structures. Despite the development of numerous processing tools, challenges in efficiently processing and analyzing complex neuronal data persist, highlighting the need for more advanced and integrated solutions. The SWCGEOM toolkit is designed to facilitate the processing and analysis of neuronal morphology and image data, offering a wide range of functionalities for data integrity, editing, and transformation. It supports various data formats, provides editing capabilities, and includes a framework for complex data analysis workflows. Aimed at researchers in neuroscience, SWCGEOM enhances the efficiency of data analysis, contributing to a deeper understanding of neuron structure and function.
Dengue fever is mostly prevalent in tropical and subtropical regions, where Aedes mosquitoes, the primary vectors for the virus, thrive in warm and humid environments. In West Bengal, a province in India, the typical ...
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The paper focuses on exploring the design of an ultrasonic liquid level measurement system, introducing its universal design concepts, hardware design, software design, system composition, and detection principle. Thi...
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As software demand proliferates and software size and complexity increase, traditional software development models face enormous challenges. As a result, new software development techniques are being explored to meet ...
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Efficient software maintenance and evolution rely heavily on effective software traceability, which is crucial for understanding the relationships between code elements and their corresponding requirements. However, e...
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The Manual Library Management System used at present is quite complex process which might take lot of time as it includes maintaining the records of personal, membership and fine details where there can be chances of ...
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In this paper, we present a comprehensive empirical study to evaluate four prominent computer Vision inference frameworks. Our goal is to shed light on their strengths and weaknesses and provide valuable insights into...
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ISBN:
(纸本)9798400704451
In this paper, we present a comprehensive empirical study to evaluate four prominent computer Vision inference frameworks. Our goal is to shed light on their strengths and weaknesses and provide valuable insights into the challenges of selecting the right inference framework for diverse situations. Additionally, we discuss the potential room for improvement to accelerate inference computing efficiency.
In modern softwareengineering education, team formation is crucial for mimicking real-world collaborative scenarios and boosting project-based learning outcomes. This paper introduces a simple, innovative, and univer...
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ISBN:
(纸本)9798400706004
In modern softwareengineering education, team formation is crucial for mimicking real-world collaborative scenarios and boosting project-based learning outcomes. This paper introduces a simple, innovative, and universally adaptable method for forming student teams within a softwareengineering class. We utilize publicly available pre-class GitHub metrics as our input variables (e.g., number of commits, pull requests, code size, etc.). For team formation, the constrained k-means algorithm is employed. This algorithm embraces domain-specific constraints, ensuring the resulting teams not only resonate with the inherent data clusters but also meet educational requirements. Preliminary results suggest that our methodology yields teams with a harmonious blend of skills, experiences, and collaborative potentials, thereby setting the stage for enhanced project success and enriched learning experiences. Quantitative analyses show that teams formed via our approach outperform both randomly assembled teams and student self-selected teams concerning project grades. Moreover, teams created using our method also display a reduced standard deviation in grades, suggesting a more consistent performance across the board.
Artificial intelligence (AI) is revolutionizing software development by enabling high-quality products at lower costs and faster delivery. While existing research highlights ChatGPT’s role in various phases of softwa...
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software fault prediction (SFP) is becoming increasingly important in softwareengineering, especially in service-oriented systems (SOS). This study investigates the effectiveness of using source code for fault predic...
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software fault prediction (SFP) is becoming increasingly important in softwareengineering, especially in service-oriented systems (SOS). This study investigates the effectiveness of using source code for fault prediction in SOS. It uses supervised machine learning algorithms such as random forest, decision tree, and support vector machine to improve error prediction accuracy. Feature extraction is used for more accurate analysis. The study highlights the strengths and weaknesses of these algorithms, providing insights into the prediction of malicious software in SOS. It aims to provide high-performance and reliable software architecture, and advance fault prediction models in SFP.
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