835 research outputs found

    Reliability Estimation Model for Software Components Using CEP

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    This paper presents a graphical complexity measure based approach with an illustration for estimating the reliability of software component. This paper also elucidates how the graph-theory concepts are applied in the field of software programming. The control graphs of several actual software components are described and the correlation between intuitive complexity and the graph-theoretic complexity are illustrated. Several properties of the graph theoretic complexity are presented which shows that the software component complexity depends only on the decision structure. A symbolic reliability model for component based software systems from the execution path of software components connected in series, parallel or mixed configuration network structure is presented with a crisp narration of the factors which influence computation of the overall reliability of component based software systems. In this paper, reliability estimation model for software components using Component Execution Paths (CEP) based on graph theory is elucidated

    Emotion Based Prediction in the Context of Optimized Trajectory Planning for Immersive Learning

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    In the virtual elements of immersive learning, the use of Google Expedition and touch-screen-based emotion are examined. The objective is to investigate possible ways to combine these technologies to enhance virtual learning environments and learners emotional engagement. Pedagogical application, affordances, and cognitive load are the corresponding measures that are involved. Students will gain insight into the reason behind their significantly higher post-assessment Prediction Systems scores compared to preassessment scores through this work that leverages technology. This suggests that it is effective to include emotional elements in immersive learning scenarios. The results of this study may help develop new strategies by leveraging the features of immersive learning technology in educational technologies to improve virtual reality and augmented reality experiences. Furthermore, the effectiveness of immersive learning environments can be raised by utilizing magnetic, optical, or hybrid trackers that considerably improve object tracking.Comment: 5 pages, 5 figure

    Optimized Deep Learning Models for AUV Seabed Image Analysis

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    Using autonomous underwater vehicles, or AUVs, has completely changed how we gather data from the ocean floor. AUV innovation has advanced significantly, especially in the analysis of images, due to the increasing need for accurate and efficient seafloor mapping. This blog post provides a detailed summary and comparison of the most current advancements in AUV seafloor image processing. We will go into the realm of undersea technology, covering everything through computer and algorithmic advancements to advances in sensors and cameras. After reading this page through to the end, you will have a solid understanding of the most up-to-date techniques and tools for using AUVs to process seabed photos and how they could further our comprehension of the ocean floorComment: 6 pages , 4 figure

    Revolutionizing Underwater Exploration of Autonomous Underwater Vehicles (AUVs) and Seabed Image Processing Techniques

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    The oceans in the Earth's in one of the last border lines on the World, with only a fraction of their depths having been explored. Advancements in technology have led to the development of Autonomous Underwater Vehicles (AUVs) that can operate independently and perform complex tasks underwater. These vehicles have revolutionized underwater exploration, allowing us to study and understand our oceans like never before. In addition to AUVs, image processing techniques have also been developed that can help us to better understand the seabed and its features. In this comprehensive survey, we will explore the latest advancements in AUV technology and seabed image processing techniques. We'll discuss how these advancements are changing the way we explore and understand our oceans, and their potential impact on the future of marine science. Join us on this journey to discover the exciting world of underwater exploration and the technologies that are driving it forward.Comment: 7 page

    Evaluation of Consumer Behavior Regarding Food Delivery Applications in India

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    . This paper explores consumer behavior towards food delivery apps, focusing on attributes like restaurant variety, food packaging quality, and application design and user interface. The study reveals that a diverse range of restaurants positively influences consumer satisfaction, leading to increased app usage. Conversely, the quality of food packaging does not significantly impact overall satisfaction. However, the study underscores the crucial role of application design and user interface in shaping consumer behavior. Both factors significantly influence overall satisfaction, with user-friendly interfaces attracting more users and promoting frequent app usage. The findings emphasize the importance of businesses addressing these attributes to enhance customer satisfaction, boost app engagement, and foster long-term customer loyalty. Understanding and catering to consumer preferences in these areas can contribute to the success of food delivery apps in the competitive market.Comment: 12 Pages,6 Figures , 2 Table

    Deep Learning based CNN Model for Classification and Detection of Individuals Wearing Face Mask

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    In response to the global COVID-19 pandemic, there has been a critical demand for protective measures, with face masks emerging as a primary safeguard. The approach involves a two-fold strategy: first, recognizing the presence of a face by detecting faces, and second, identifying masks on those faces. This project utilizes deep learning to create a model that can detect face masks in real-time streaming video as well as images. Face detection, a facet of object detection, finds applications in diverse fields such as security, biometrics, and law enforcement. Various detector systems worldwide have been developed and implemented, with convolutional neural networks chosen for their superior performance accuracy and speed in object detection. Experimental results attest to the model's excellent accuracy on test data. The primary focus of this research is to enhance security, particularly in sensitive areas. The research paper proposes a rapid image pre-processing method with masks centred on faces. Employing feature extraction and Convolutional Neural Network, the system classifies and detects individuals wearing masks. The research unfolds in three stages: image pre-processing, image cropping, and image classification, collectively contributing to the identification of masked faces. Continuous surveillance through webcams or CCTV cameras ensures constant monitoring, triggering a security alert if a person is detected without a mask.Comment: 8 Pages , 6 figures , 1 Tabl

    Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer

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    With the proliferation of related microarray studies by independent groups, a natural step in the analysis of these gene expression data is to combine the results across these studies. However, this raises a variety of issues in the analysis of such data. In this article, we discuss the statistical issues of combining data from multiple gene expression studies. This leads to more complications than those in standard meta-analyses, including different experimental platforms, duplicate spots and complex data structures. We illustrate these ideas using data from four prostate cancer profiling studies. In addition, we develop a simple approach for assessing differential expression using the LASSO method. A combination of the results and the pathway databases are then used to generate candidate biological pathways for cancer.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47935/1/10142_2003_Article_87.pd

    The long non-coding RNA PCAT-1 promotes prostate cancer cell proliferation through cMyc.

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    Long non-coding RNAs (lncRNAs) represent an emerging layer of cancer biology, contributing to tumor proliferation, invasion, and metastasis. Here, we describe a role for the oncogenic lncRNA PCAT-1 in prostate cancer proliferation through cMyc. We find that PCAT-1-mediated proliferation is dependent on cMyc protein stabilization, and using expression profiling, we observed that cMyc is required for a subset of PCAT-1-induced expression changes. The PCAT-1-cMyc relationship is mediated through the post-transcriptional activity of the MYC 3\u27 untranslated region, and we characterize a role for PCAT-1 in the disruption of MYC-targeting microRNAs. To further elucidate a role for post-transcriptional regulation, we demonstrate that targeting PCAT-1 with miR-3667-3p, which does not target MYC, is able to reverse the stabilization of cMyc by PCAT-1. This work establishes a basis for the oncogenic role of PCAT-1 in cancer cell proliferation and is the first study to implicate lncRNAs in the regulation of cMyc in prostate cancer
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