检索结果-内蒙古大学图书馆

4th International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2024

作者： Prince Sekwatlakwatla, Sello Malele, Vusumuzi North-West University Unit for Data Science and Computing Department of Computer Science and Information Systems South Africa

ISBN: (纸本)9798350387988

Cloud computing solutions are becoming more and more popular as a way for organizations to improve productivity, save costs, and simplify procedures. The advantage of cloud services is that they enable users to store data remotely and access on-demand applications and services from a shared pool of configurable computing resources. Cloud computing resource allocation faces challenges in load balancing, effective resource management, and compliance with regulatory and legal requirements, requiring complex tools and techniques for optimal use. Most organizations are moving to cloud services and encouraging their clients to access services online. However, effective resource allocation is critical for improving performance and lowering costs in this area. Due to unpredictable network traffic in cloud computing, resource allocation is challenging, which causes customers to complain about application timeouts, delayed system times, and higher bandwidth use during peak hours. This entails allocating resources to various users and programs, such as memory, processing power, storage, and network bandwidth. In this regard, this study compares the ensemble method, which is stepwise Gaussian Linear Autoregressive (SGLA) and the individual method, which is autoregressive integrated moving average (ARIMA). The results show SGLA prediction accuracy increased with an average of 97.9%, and ARIMA prediction showed an accuracy of 71.5%. In this regard the ensemble method performed better than individual methods using the same datasets. The study recommends the ensemble method for the prediction and allocation of resources in cloud computing. © 2024 IEEE.

关键词： Cloud platforms

来源：评论

学校读者我要写书评

暂无评论

Promo-Zen: A Natural Language Processing Based Promotional Text Message Management System 6

Promo-Zen: A Natural Language Processing Based Promotional T...

引用

6th International Conference on Advancements in Computing, ICAC 2024

作者： Udugampola, Chamath Weerasingha, Nethmi NSBM Green University Department of Computer and Data Science Homagama Sri Lanka

ISBN: (纸本)9798331517878

Promotional Short Message Service (SMS) messages, which provide frequent updates about deals and discounts to consumers, are crucial in developing countries like Sri Lanka, as they help alleviate financial pressure during economic crises. However, poor practices by the brands, SMS technology limitations and human memory limitations challenge the ability of consumers to make effective use of this facility. As a solution, this project aimed to develop an Android application to organize and enhance the readability of promotional messages, while also providing mechanisms to prevent users from forgetting promotions. These objectives were achieved by creating a Kotlin app that displays promotional information in a date-sorted weekly dashboard, offers deal-expiration reminders, and optimizes message content for readability. The system utilizes a novel workflow/pipeline combining a Natural Language Processing (NLP) model with Named Entity Recognition (NER) capabilities, Regular Expressions and custom logic, to understand the context of and extract the crucial information from the promotional messages. User-acceptance testing carried out demonstrates positive results, reducing traditional access times to promotions of 8-10 seconds to approximately 2 seconds through this application. Additionally, user-testers reported multiple occurrences of the reminders feature ensuring that promotions weren't forgotten before use. © 2024 IEEE.

关键词： Acceptance tests

来源：评论

学校读者我要写书评

暂无评论

Plant Fettle Detector: Unifying Fruit Quality Tracking and Plant Disease Detection through Advanced Image Analysis 3

Plant Fettle Detector: Unifying Fruit Quality Tracking and P...

引用

3rd International Conference for Innovation in Technology, INOCON 2024

作者： Nair, Sruthi Bajpai, Khushi Fadnavis, Sakshi Jain, Shruti Sheikh, Zeenat Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering - Data Science Nagpur44013 India

ISBN: (纸本)9798350381931

India being an agricultural country, food quality tracking is a major challenge faced by common farmers across the country. This research presents an innovative integration of Convolutional Neural Networks (CNNs) to address key challenges in agriculture - fruit quality tracking and plant disease detection. The unified system comprises the Fruit Quality Tracker and the Plant Disease Detector, leveraging advanced image analysis for precision agriculture. The Fruit Quality Tracker uses computer vision to assess ripeness and defects in real-time, empowering farmers with informed decisions. Simultaneously, the Plant Disease Detector swiftly identifies and classifies diseases, mitigating economic losses. The research explores architectural nuances, development methodologies, and deployment on Google Cloud Platform. Testing shows high accuracy rates of 98.5% for fruit quality and 98.83% for plant disease detection. The system's transformative potential in agriculture is highlighted, bridging the gap between technology and sustainable food production systems. © 2024 IEEE.

关键词： Image analysis

来源：评论

学校读者我要写书评

暂无评论

Risk Identification of data science Projects: A Literature Review

Risk Identification of Data Science Projects: A Literature R...

引用

2023 Learning. Knowledge. data. Analytics, LWDA 2023

作者： Holtkemper, Maike Potanin, Maria Oberst, Alexander Beecks, Christian University of Hagen Department of Mathematics and Computer Science Chair of Data Science Germany

While cost, time, and resources are considered to have a high impact on data science projects, risks are the key for the successful implementation of a project. The correct handling of risks has been proven to increase a project’s chances of success. Therefore, awareness of existing and emerging risks as well as their assessment play a major role in data science project management. In 2021, 87 percent of data science projects fail and are thus not implemented successfully. The path of successful implementation of data science projects in companies is more complex and uncertain than for conventional projects, in particular the identification and availability of the necessary skills in a company before and during the project. Regarding software engineering projects, the analysis and evaluation of potential risks is well-known, but for data science projects potential risks have not yet been analyzed at a larger scale. To identify the potential reasons for failure of data science projects, an in-depth understanding of potential risks and measures for mitigation these risks is inevitable. In this paper, we conduct a systematic literature review on risks of data science projects and present the top fifteen risks as first findings. Furthermore, we compare the identified risks to the major risks of software engineering projects to highlight similarities and differences between these two disciplines. The findings of our literature review can be used in guiding the development of future risk assessment systems for complex data science projects. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Risk assessment

来源：评论

学校读者我要写书评

暂无评论

Deep Learning-Based Holistic Speaker Independent Visual Speech Recognition

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2023年第6期4卷 1705-1713页

作者： Nemani, Praneeth Krishna, Ghanta Sai Ramisetty, Nikhil Sai, B Digvijay Sri Kumar, Santosh IIIT Naya Raipur Department of Computer Science and Engineering Raipur493661 India IIIT Naya Raipur Department of Data Science and Artificial Intelligence Raipur493661 India

From a broader perspective, the objective of visual speech recognition (VSR) is to comprehend the speech spoken by an individual using visual deformations. However, some of the significant limitations of existing solutions include the dearth of training data, improper end-to-end deployed solutions, lack of holistic feature representation, and less accuracy. To resolve these limitations, this study proposes a novel, scalable, and robust VSR system that uses the videotape of the user to determine the word which is being spoken. In this regard, a customized 3-D convolutional neural network (3-D CNN) architecture is proposed by extracting the spatio-temporal features and eventually mapping the prediction probabilities of the elements in the corpus. We have created a customized dataset resembling the metadata contained in the MIRACL-VC1 dataset to validate the concept of person-independence. While being robust to a broad spectrum of lighting conditions across multiple devices, our model achieves a training accuracy of 80.2% and a testing accuracy of 77.9% in predicting the word spoken by the user. © 2020 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

RLHR: A Framework for Driving Dynamically Adaptable Questionnaires and Profiling People Using Reinforcement Learning

RLHR: A Framework for Driving Dynamically Adaptable Question...

引用

19th International Conference on Software Technologies, ICSOFT 2024

作者： Paduraru, Ciprian Patilea, Catalina Camelia Stefanescu, Alin Department of Computer Science University of Bucharest Romania Institute for Logic and Data Science Romania

ISBN: (纸本)9789897587061

In today’s corporate landscape, the creation of questionnaires, surveys or evaluation forms for employees is a widespread practice. These tools are regularly used to check various aspects such as motivation, opportunities for improvement, satisfaction levels and even potential cybersecurity risks. A common limitation lies in their generic nature: they often lack personalization and rely on predetermined questions. Our research focuses on improving this process by introducing AI agents based on reinforcement learning. These agents dynamically adapt the content of surveys to each person based on their unique personality traits. Our framework is open source and can be seamlessly integrated into various use cases in different industries or academic research. To evaluate the effectiveness of the approach, we tackle a real-life scenario: the detection of potentially inappropriate behavior in the workplace. In this context, the reinforcement learning-based AI agents function like human recruiters and create personalized surveys. The results are encouraging, as they show that our decision algorithms for content selection are very similar to those of recruiters. The open-source framework also includes tools for detailed post-analysis for further decision making and explanation of the results. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Launching a data Analytics Major: How Collaboration and Agility Built a Successful Program

引用

American Mathematical Monthly 2025年第1期132卷 77-88页

作者： Forrester, Richard Marshall, Emily Forrester, Jeffrey Richeson, David Schaefer, Jennifer Department of Data Analytics and Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States Department of Economics Denison University Granville 43023 OH United States Department of Data Analytics and Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States Department of Mathematics and Computer Science Dickinson College Carlisle 17013 PA United States

In this article, we share the details of Dickinson College’s journey to establish a data analytics major. Designed to provide students with the technical proficiency required to become a data scientist, Dickinson’s data analytics major also incorporates values of a liberal arts education, including interdisciplinary learning, critical thinking, thoughtful analysis, effective communication, and ethical considerations. We describe how sustained collaboration, a detailed plan to engage the campus community, partnerships with alumni, and support from senior leaders were essential to this achievement. We also discuss the challenges and successes encountered before and after the approval of the major. © 2024 Mathematical Association of America.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Polyp Detection Using U-Net Neural Network Based Algorithm 2

Polyp Detection Using U-Net Neural Network Based Algorithm

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Bipin Kumar Singh, Devansh Shukla, Archita Dayananda Sagar University Department of Computer Science & Engineering Bengaluru India Abes Institute of Technology Department of Computer Science & Engineering-Data Science U.P. Ghaziabad India

ISBN: (纸本)9798350371055

Polyp is an earlier stage of cancer development in gastro-intestinal tract. Despite the fact that numerous techniques for automatic segmentation and detection of polyps have been developed, it still remains an open problem. Examining color images captured by the board camera in endoscopy is contemplated the most reliable method for detectingdeveloping polyps. To automate this process, in this paper we present a modified U-Net Neural Network based algorithm. This algorithm is a binary classifier which labels the pixels as part of the polyp or not. This model can analyze the video of GItract frame by frame and produce the collection of frames with possibility of presence of polyp thus reducing the time required to process the GI-tract. Detecting, localizing, and segmenting polyps using Kvasir-SEG, an open-access dataset of colonoscopy images, we benchmark various modern day approaches in this work, assessing bothmethod speed and accuracy. This is attributed to outstanding performance of image classification compared to preceding techniques. The automatically identifying polyps serves to aid gastroenterologists during colonoscopies. While existing literature contains publications addressing the challenge of polyp detection, many of these systems remain confined toresearch settings and lack implementation for clinical use. Hence, we present the inaugural fully open-source automated polyp detection system, which not only outperforms the best- performing system documented in the literature but is also prepared for immediate clinical application. © 2024 IEEE.

关键词： Colonoscopy ColonSegNet Colorectal cancer Kvasir-SEG Medical image segmentation Polyp Detection U-net

来源：评论

学校读者我要写书评

暂无评论

Diabetic Retinopathy Detection using Deep Learning Model ResNet15 2

Diabetic Retinopathy Detection using Deep Learning Model Res...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Bipin Kumar Ojha, Harsh Srivastava, Ishani Dayananda Sagar University Department of Computer Science & Engineering Bengaluru India Abes Institute of Technology Department of Computer Science & Engineering- Data Science U.P. Ghaziabad India

ISBN: (纸本)9798350371055

Diabetic Retinopathy stands out as one of the common retinal diseases which has a significant threat to vision and can lead to blindness. Several problems occurred due to DR can be stopped by controlling blood glucose and timely treatment, but the manual assessment of retinal images is often a lengthy process and can prevail human errors. This paper attempts in the direction of finding an automated way to classify the set of retinal images. The study starts with the collection of retinal images and pre- processing them, followed by training of a CNN model, Utilizing Deep Learning methodologies, CNN classifies the degree of diabetic retinopathy (DR) into four categories: 0 (no DR) to 4 (proliferative DR). We evaluate our model's performance using a diverse dataset. We use Convolutional Neural Networks (CNNs) strength to DR detection, which involves three major difficulties: Classification, Segmentation and Detection. We work towards developing a GUI based system to store and maintain a series of predictions with the patient's id and name. Twilio API have been used to make SMS connectivity to patients possible in case they are not accessible. Many research activities in the previous years have many limitations and disadvantages like models used other than Resnet have low accuracy, not capturing intricate features and patterns in images, Vanishing Gradient Descent and low performance for image classification. Resnet models that are previously used in DR detection with Resnet152, we can conclude that it has the lowest Training loss, lowest Test loss and it has the lowest loss Difference. We have worked on a RESNET-152 pre-trained deep learning model, which consists of thousands of layers and allows for the capturing of intricate features, high performance of image classification, and high accuracy, in order to overcome the issues and limitations of earlier developed models. © 2024 IEEE.

关键词： Deep Learning Diabetic Retinopathy (DR) Fundus images Ophthalmologist Proliferative DR Resnet152

来源：评论

学校读者我要写书评

暂无评论

data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

引用

computers, Materials & Continua 2025年第4期83卷 407-434页

作者： Tien-Wen Sung Wei Li Chao-Yang Lee Yuzhen Chen Qingjun Fang Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and MathematicsFujian University of TechnologyFuzhou350118China Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin640301TaiwanChina

To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the *** number of DAPs installed and the installation location greatly impact the whole *** the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the ***,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the *** address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different *** algorithm adds an allocation mechanism to optimize the subnetwork in the *** is evaluated under three different areas and compared with other DAP placement *** experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.

关键词： Smart grid data aggregation point placement network cost average transmission distance load gap

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：