835 research outputs found
Reliability Estimation Model for Software Components Using CEP
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
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
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
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
. 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
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
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.
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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