检索结果-内蒙古大学图书馆

2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Siva Rama Lingham, N. Parameswari, M. Karthick, P. Priya, T. Subha Rathi Rajesh, G. Jagannathan, J. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Chennai India School of Engineering and Technology Jeppiaar University Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Vellore Katpadi632014 India

ISBN: (纸本)9798350328202

Twitter has been observed to be one of the essential data resources for dependable event accreditation. In any case, Twitter-based event affirmation structures can't guarantee assessment concerning their attestation results. Talk statement has been locked in starting late to enable liberal event Recognition. This problem is not yet evident in light of the assumption that most people tend to think that they have a specific limit to the data on Twitter that have frames based on events based on Twitter. Current appraisals see new pieces by seeing and looking at the significant psychological aspects of hitting the details on Twitter. No matter, the scale of the visual cues can be well established, with the intention that it is essential to design an integrated Twitter experiment with another set of extensible data resources to rectify this problem. The problem is the processes by which you should wholeheartedly investigate all converted data because it has different data structures, transfer times, etc. This issue of address, the paper proposes a framework for evaluating the use of Twitter-based events and examines two types of data resources for the reliability of impact testing. Our framework uses the resultant of the request obtaining by various events and relevant to the articles on it. This paper has improved the cleaning test with the proposed process how the integrity estimation of twitter based event can be recognized with the help of group of analyzed data. The evaluation indicates that the proposed system provides visible high-level testing events and a variety of low-level testing events. © 2024 IEEE.

关键词： Impact testing

来源：评论

学校读者我要写书评

暂无评论

Face Recognition Based on Fusion Feature Extraction Algorithms 6

Face Recognition Based on Fusion Feature Extraction Algorith...

引用

6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Albakri, Ibrahim Mahmood Rashid Ahmad, Muhammad Imran Md Isa, Mohd Nazrin Al-Dabagh, Mustafa Zuhaer Nayef Universiti Malaysia Perlis Faculty of Electronic Engineering Technology Perlis Malaysia Collage of Agriculture Telafer University Telafer Iraq Knowledge University Department of Computer Science Erbil Iraq

ISBN: (纸本)9798350306613

Biometric-based methods have emerged as a highly promising avenue for individual recognition, with face recognition standing out as a key application in image analysis. Facial recognition systems are affected by various image condition factors, including expressions, occlusion, poses, and illuminations. It is crucial to consider reasonable variations in illumination between gallery and probe images when developing face recognition algorithms. In the context of face verification, illumination variation significantly influences the likelihood of misclassification. Existing research lacks a focus on imaging conditions, especially regarding illumination, in face recognition systems. This research aims to identify and design a scheme to address illumination variation in facial recognition. The proposed scheme will undergo analysis and evaluation to assess its accuracy and effectiveness. The image processing in this research is divided into two phases. The first is the preprocessing phase, encompassing auto contrast balancing and noise reduction. The second phase is face processing, which involves illumination normalization, face detection, feature extraction, and face recognition. Two datasets are utilized: The first being the ORL dataset, which includes images with illumination variations, and the second being the Yale database, where images contain various noises. © 2024 IEEE.

关键词： FACE RECOGNITION Feature Extraction Fusion HOG LBP

来源：评论

学校读者我要写书评

暂无评论

Pancreatic Cancer Prediction Machine Learning using Multi Fusion Model

Pancreatic Cancer Prediction Machine Learning using Multi Fu...

引用

2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Sathiyapriya, V. Devi, K. Nethra Deepa, S. Marimuthu, M. Manikandan, P. Prabu, S. Knowledge Institute of Technology Department of Computer Science and Engineering Kakapalayam India Gnanamani College of Technology Department of Artificial Intelligence and Data Science Namakkal India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Tiruchirappalli India

ISBN: (纸本)9798350375442

Pancreatic cancer poses a significant challenge in early detection and treatment due to its malignant nature within the digestive tract. Recent studies, such as those conducted by the Pancreatic Cancer Action Network, predict that pancreatic cancer will rank as the world's second leading cause of cancer-related deaths in 2020. Various methods have been explored in the literature to segment pancreatic tumors and detect lesions from CT images, crucial for assessing severity. One such method is the Min-Max segmentation algorithm, which effectively removes the jaundiced sclera region from affected eyes by analysing color gradients, offering more feasible and discriminative solutions. Additionally, the utilization of Whale Optimized feature-based selection aids in extracting optimal features for optimization purposes. Through an analysis of different classification algorithms and the implementation of a suggested technique for achieving optimum solutions and maximum accuracy, the proposed work is structured into two phases. Phase I involves pre-processing and artifact removal, followed by segmentation, while Phase II focuses on feature extraction, feature selection, and classification. The initial step of pre-processing the image facilitates pixel location using intensity measures, crucial for accurate tumor segmentation and minimizing false detection. Ultimately, the proposed approach aims to design and develop an optimal feature selection strategy for classifying pancreatic cancer tumors in PET/CT images, resulting in increased classification efficiency 92% and improved cancer tumor detection rates with compare other existing approaches. © 2024 IEEE.

关键词： computerized tomography

来源：评论

学校读者我要写书评

暂无评论

Enhancing reliability in coronary artery disease prediction using ensemble learning for key attribute identification

引用

Life Cycle Reliability and Safety engineering 2025年 1-14页

作者： Upadhyay, Sachin Sagar, Anil Kumar Roy, Nihar Ranjan Department of Computer Science and Engineering School of Engineering and Technology Sharda University Knowledge Park III Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology VIPS-TC Delhi Pitampura India

Cardiovascular disease (CAD) is a significant public health concern, affecting a large population worldwide. Early diagnosis and management of CAD can minimize the risk of acute myocardial infarction and improve patient outcomes. Assessment tools like SBP, cholesterol, pulse rate, and ST segment depression can help identify causes early and manage them effectively. Management includes medication therapy, healthy dietary habits, and exercise. Several machine learning (ML) methodologies have been researched to enhance CAD predictions, including AdaBoost, ANNs, J48, Decision Tree, K Nearest Neighbor (KNN), Naïve Bayes, and Random Forest. However, single models still lack sufficient capacity to address the complexity and flexibility of CAD. Ensemble learning, which uses multiple classifiers to boost predictability, has been employed to address these issues. The system was developed after benchmarking it with multiple classifiers on a Cleveland cardiac disease dataset. The ensemble method showed a 92.11% accuracy rate, far better than the highest performing classifier operating individually. This suggests the possibility of practical solutions for CAD prediction using ensemble methods, enabling precise early diagnosis and efficient targeted treatment. Comparing ensemble learning for CAD predictors reveals how these approaches can revolutionize medicine by enabling early diagnosis and personalized treatment plans. There is a need to further develop these methods for clinical application, such as creating practical tools for easier application by healthcare workers and integrating sophisticated techniques. In conclusion, ensemble learning methods represent significant advancements in CAD prediction, with superior performance in identifying critical attributes and enhancing predictive accuracy. As healthcare evolves with the integration of intelligent technologies, the adoption of ensemble learning methods holds great promise for enhancing patient outcomes and reducing the

关键词： ANN CAD Decision Trees Ensemble KNN Machine learning Random Forest

来源：评论

学校读者我要写书评

暂无评论

Innovative Applications of AI and IoT in Smart Urban Infrastructure and Housing Projects 2

Innovative Applications of AI and IoT in Smart Urban Infrast...

引用

2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Jasim, Jasim Ahmede Hilal Mutlag, Ali Othman, Nashwan Adnan Shareef, Hayder Hameed, Nema H. Al-Nisour University College Baghdad Iraq Al Kitab University Department of Medical Engineering Kirkuk Iraq Knowledge University College of Engineering Department of Computer Engineering Erbil Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Uruk University Department of Anesthesia Techniques Baghdad Iraq

ISBN: (纸本)9798331534950

The Internet of Things (IoT) is a new segment of the global web that will experience quick and massive network growth. Proponents of the Internet of Things see all gadgets being networked and managed by a central authority. Hybrid AI (Artificial Intelligence) technologies were increasingly used while building risk reduction techniques. The objectives are to determine the popularity and acceptance of IoT in the building industry, investigate key stakeholders' reactions, and identify barriers in the IoT decision-making process. This article explores the core concept of the Internet as it applies to construction projects, evaluations, and the use of IoT and AI in civil engineering. The purpose of this research is to highlight the different applications of the Internet in the development of smart cities and communities. In this context, a proposed AI with IoT (CAI) method is demonstrated, allowing us to examine how the foundational industry is employed, as well as its concerns and challenges. Capturing high-uncertainty risk and complicated relationships is a main goal of AI-based cost-risk assessment in the construction industry. This article provides future network potential for innovation, and it improves understanding of numerous prospective applications in civil engineering projects. The experiment results show that the ratio of analyzing big data is 85.5%, the percentage of improving facility management is 90.9%, the proportion of manufacturing efficiency is 91.7%, the percentage of improving worker safety measures is 84.2%, the ratio of managing architecture is 90.9%, and the ratio of evaluating the development stage is 92.6%. © 2024 IEEE.

关键词： Artificial Intelligence Buildings Civil engineering Construction Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Leveraging Social Media for Collaborative English Language Learning 2

Leveraging Social Media for Collaborative English Language L...

引用

2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Jawad, Israa Abed Burhan Rajab, Adnan Sharif, Salah Farhan A Hussain, Ahmed Daham Wassif, Sattar Jabar Al-Nisour University College Baghdad Iraq College of Engineering Knowledge University Department of Computer Engineering Erbil Iraq Al Kitab University Department of Medical Engineering Kirkuk Iraq Al-FarahidiUniversity Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Uruk University Department of Medical Laboratory Techniques Baghdad Iraq

ISBN: (纸本)9798331534950

Social networking is important for students who are learning English because it allows them to improve their reading and writing skills while simultaneously discovering new texts and expressions to broaden their vocabulary. On a number of sites, educators can use social media to teach English. Every platform has a number of benefits for language learners. When English teachers choose to use social media in the classroom, they must first identify which social media sites will improve their students' performance and what resources they may use to assist students experience English. Higher education students face a number of challenges when utilizing social media, such as time wastage, antisocial behavior, inappropriate use of technology in class, and security issues. To get over these problems, the CL-ELT-SM method - collaborative learning for English language teaching utilizing social media - has been proposed. The suggested approach will examine the most widely used social media platform for English language instruction and go over how teachers might use it in the classroom. Students and teachers can learn more quickly and effectively by connecting educational apps and services, such as Facebook groups and Twitter, with other educational platforms. Students can post content and interact with others on these networks by using social media plugins. A deeper comprehension of the usage of social media for educational purposes is provided by the CL-ELT-SM technique. According to the trial results, the suggested approach improves student performance and reaches a 96.3% level in e-learning interaction between students and teachers. © 2024 IEEE.

关键词： Collaborative learning

来源：评论

学校读者我要写书评

暂无评论

Optimizing Fog Network Performance through Task Distribution in Deep Neural Networks 2

Optimizing Fog Network Performance through Task Distribution...

引用

2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Qasim, Mohammed Nada Hussain, Faris Hassan Taha Hameed, Nema H. Rajab, Adnan Burhan Al-Taee, Mustafa M. Al-Nisour University College Baghdad Iraq Al Kitab University Department of Medical Engineering Kirkuk Iraq Uruk University Department of Anesthesia Techniques Baghdad Iraq Knowledge University College of Engineering Department of Computer Engineering Erbil Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq

ISBN: (纸本)9798331534950

The transition from Internet of Things (IoT) devices to fog nodes in distributed computing addresses IoT's resource limitations, cost-effectiveness, and productivity issues by delegating duties. Despite constrained resources, fog nodes can address the increasing demands for mobile data and compute requirements in IoT applications and Deep Neural Networks (DNNs). Nonetheless, attaining optimal outcomes with conventional optimization techniques is difficult, notably when end-users are excluded from the decision-making process. Due to challenges in accessing original network parameters, deep learning methodologies encounter constraints in adjusting to novel situations. To tackle these difficulties, we present the Deep Neural Network-based Task Offloading Framework (DNNTOF), which utilizes deep learning's perceptual capabilities to execute adaptive, real-time offloading decisions. Simulations indicate that DNNTOF surpasses traditional deep learning methods, facilitating effective job offloading in dynamic and mobile IoT environments. © 2024 IEEE.

关键词： Fog computing

来源：评论

学校读者我要写书评

暂无评论

Challenges and Pathways to an Integrated Curriculum in IoT and Data Security 2

Challenges and Pathways to an Integrated Curriculum in IoT a...

引用

2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Alanssari, Ali Ihsan Majed, Aaied Eqab Muraay Jaafar, Mohammed M. Myassar, Hussain Sulaiman, Sazan Kamal Al-Nisour University College Baghdad Iraq Al Kitab University Department: Medical Engineering Kirkuk Iraq Uruk University Department of Optical Techniques Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Knowledge University College of Engineering Department of Computer Engineering Erbil Iraq

ISBN: (纸本)9798331534950

The development of society is dependent on educated citizens, who are essential to all countries. ICT and employing cutting-edge technologies in formal education have created a distinct aspect of education. This course fosters learning through skill acquisition, adaption, and technological application. Intelligent education supports cloud computing, learning analysis, large-scale data, IoT, portable technology, etc. Many believe universities should prioritize data security, but few do. However, since technology controls information and security breaches, educational institutions are establishing data security rules. This article introduces Holistic Education and IoT Data Security Education (HE-IDS) to examine IoT requirements and the innovative training model and architecture that creates voluminous data. Holistic development, an integrated learning strategy, promotes a varied and capable mind. Holistic development develops a person's physical health, mental clarity, emotional stability, and social abilities. Parents now expect their children to actively participate in most academic activities due to technology and instructional approaches. When considering holistic development, parenting is key. IoT-informed institutional decision-making improves student training, operational efficiency, campus safety, and more. The HE-IDS method yields: E-learning materials are 85.6%, surveys of institution strength are 83.2%, opportunities are 86.7%, security is 89.4%, educational difficulties are 91.4%, and holistic education performance is 88.6%. © 2024 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Optimizing Resource Management in 5G Heterogeneous Edge Computing with Blockchain and Deep Learning 2

Optimizing Resource Management in 5G Heterogeneous Edge Comp...

引用

2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Mohammed, Nada Qasim Izzat Hassan, Sharmeen Manee, Mariam J. Alani, Hithaifa Hama Sharif, Shukur Ramadan Al-Nisour University College Baghdad Iraq Knowledge University College of Engineering Department of Computer Engineering Erbil Iraq Uruk University Department of Radiology Techniques Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Al Kitab University Department Medical Engineering Kirkuk Iraq

ISBN: (纸本)9798331534950

The introduction of smart edge computing (SEC), cloud-based apps might be installed on the network's edge, freeing up server resources for more computationally expensive services such as facial recognition and natural language processing. The user interface and device efficiency can be improved by offloading operations normally done by the device's core CPU to the edge. Better coverage for the SEC could benefit Internet of Things (IoT) apps that require a lot of resources. Consistent, steady, and rapid interaction between edge devices and central processing units is difficult to achieve, particularly in 5G Heterogeneous Network (HN) environments where access is dynamic and network devices have varied levels of available resources. As a result, this study presents a paradigm for intelligent resource management in smart edge computing (IMRSEC) that combines blockchain, edge computing, and AI with 5G HNs. A novel dual schedule deep reinforcement learning (DS-DRL) technique was developed, which combines quick and gradual learning schedules. Reduced emptying delay and increased system reserves are major priorities, which can be done by enhancing computation unloading, resource transit, and software caching deployment. Simulation results reveal that the proposed IMRSEC is a successful DS-DRL implementation, as well as a 32% reduction in job completion time using the suggested technique. © 2024 IEEE.

关键词： 5G mobile communication systems

来源：评论

学校读者我要写书评

暂无评论

HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

引用

Data Intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

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：