检索结果-内蒙古大学图书馆

Effect of geometric heterogeneity using an auxiliary device on the accuracy of complete arch implant scanning: An in vitro study of different clinical simulations

引用

Journal of Prosthetic Dentistry 2023年

作者： Abdelrehim, Aly Sulaiman, Eshamsul Sofian, Hazrina Salleh, Nosizana Mohd Graduate student Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya (UM) Kuala Lumpur Malaysia Associate Professor Department of Restorative Dentistry Faculty of Dentistry Universiti Malaya (UM) Kuala Lumpur Malaysia Senior Lecturer Department of Software Engineering Faculty of Computer Science & Information Technology Universiti Malaya (UM) Kuala Lumpur Malaysia

Statement of problem: Intraoral scanning of implants supporting complete arch prostheses is limited because of the lack of geometric heterogeneity and unique reference points, creating inherent errors in the image stitching process by the scanner software program. Purpose: The purpose of this in vitro study was to evaluate the significance of geometric heterogeneity on complete arch implant scanning by using a novel auxiliary geometric device. Three different clinical simulations were tested to assess its significance. The study also assessed whether scans produced using the auxiliary device would meet a clinically acceptable threshold. Material and methods: A total of 60 scans (n=20) were performed using an intraoral scanner in 3 different clinical simulations: 2 parallel implants, 4 parallel implants, and 4 implants with a 30-degree posterior angulation of the distal implants. Scanning alternated between using the auxiliary geometric scanning device (test groups;4IP+, 4IA+, 2IP+) and not using the device (control groups;4IP-, 4IA-, 2IP-). A reference scan for each model was prepared from a high precision laboratory scanner. The scans were analyzed for accuracy in 3-dimensional deviation, interimplant distance deviation, and angular deviation by using an inspection software program. The effect of the auxiliary device was statistically analyzed by comparing scans of the same group using the paired t test for normally distributed data and the Wilcoxon Signed Rank test when data were not normally distributed (α=.05). Results: Significant effects of the auxiliary geometric device were found in 3-dimensional, distance and angular deviations (P<.05). Scans performed using the device were significantly more accurate in most implant positions (P<.05). Linear and angular deviations were clinically acceptable for all test groups. However, the deviations were above the clinically acceptable threshold for the control groups. Conclusions: Using an auxiliary geometric device sig

关键词：

来源：评论

学校读者我要写书评

暂无评论

Real-time multi-factor adaptive task scheduling model for improved QoS in cloud environment

引用

AIP Conference Proceedings 2024年第1期3075卷

作者： M. Baskar G. John Samuel Babu Associate Professor Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamilnadu 603 203 India. Associate Professor Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana 500043 India.

The task scheduling and data security issue in the cloud is well-studied, and different approaches exist around the problem. Most approaches consider makespan, throughput, delay, and others in task scheduling. The existing methods need better performance in various quality of service metrics as they consider only limited features of task scheduling. To overcome these issues, an efficient multi-factor adaptive task scheduling (MATS) is presented in this article. The method considers the cost, delay, and utilization of various services in selecting a service for task scheduling. According to the factors mentioned above, the method applies MFTS scheduling which approximates the factors toward maximizing the QoS performance. The MFTS algorithm computes Cost Support Measure (CSM), Delay Centric QoS Support Measure (DCQSM), and Utilization Support Measure (USM). Using all these measures, the value of Selection Weight (SW) is measured to support scheduling. Further to maximize data security, the proposed MATS model discovers the routes and estimates Quality Centric Transmission Support (QCTS) to identify an optimal route to support QoS maximization. The MATS scheduling approach achieves higher QoS performance with data security.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Automated Detection of Multiple Sclerosis Lesions in MRI with SegNet

Automated Detection of Multiple Sclerosis Lesions in MRI wit...

引用

Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Neeraj Varshney Pankaj Agrawal Anthony Rose Deepak Kumar Dewangan Kamred Udham Singh J. Somasekar Department of Computer Engineering & Apllications GLA University Mathura UP India Department Of Ele. & Tele. Raipur Institute Of Technology Raipur Institute of Management & Entrepreneurship Development Bharati Vidyapeeth (Deemed to be University) Pune Department of Computer Science and Engineering Atal Bihari Vajpayee-Indian Institute of Information Technology and Management (ABV-IIITM) Gwalior School of Computing Graphic Era Hill University Dehradun India Professor of CSE Department Jain (Deemed-to-be University) Bangalore

Using deep neural networks, the proposed approach segments MRI images into classes and achieves good visual quality as well as similarity to the ground truth image. For the proposed process the dice similarity index is near 0.8. Dice similarity index is calculated for each class i.e. for WM, GM, CSF. Also the ground truth images are compared with the reference images and segmented MRI images that are used in the paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reinforcement learning: A novel approach towards drug discovery

引用

AIP Conference Proceedings 2023年第1期2477卷

作者： Nishath Ansari Amjan Shaik Patluri Harika Boga Varasree K. Purnachand Saikumar Tara Assistant Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Assistant Professor Department of Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Assistant Professor Department of Information Technology Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Associate Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Associate Professor Department of Electronics and Communication Engineering CMR Technical campus Hyderabad Telangana India

Reinforcement learning has been giving new strategies to ad-lib in the fields of medication and the drug industry. It is probably the best strategy among all the AI strategies. Reinforcement learning has been compelling to discover strategies that could treat the immense number of deadly infections. In this paper, a detailed new methodology about what Reinforcement Leaning and what is drug revelation and what are various techniques utilized in Reinforcement learning, how Reinforcement learning could be utilized in different utilization of medication plan such as target identification, hit discovery, hit to lead, lead optimization. The consequence of the paper illustrates the summarized data of different approaches for the development and discovery of drugs. A clear illustration of various work done in the field of drug discovery, discuss different pros and cons of Reinforcement learning as well emphasize primary challenges need to handle and how to defeat them in the future.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Optimizing Hindi Paragraph Summarization through PageRank Method

Optimizing Hindi Paragraph Summarization through PageRank Me...

引用

International Conference on Edge Computing and Applications (ICECAA)

作者： Sheshang Degadwala Dhairya Vyas Khush Nilaykumar Patel Mukesh Soni Pavitar Parkash Singh Ramya Maranan Associate Professor & Head of Computer Engineering Department Sigma University Vadodara Gujarat India The Maharaja Sayajirao University of Baroda Vadodara Gujarat India Charotar University of Science and Technology Anand Gujarat Department of CSE University Centre for Research & Development Chandigarh University Mohali Punjab India Lovely Professional University Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

This research paper presents a novel approach to optimize Hindi paragraph summarization using the PageRank method. With the increasing volume of Hindi language content on the internet, there is a growing need for effective summarization techniques tailored specifically for Hindi text. The proposed method leverages the inherent structure and connectivity of Hindi paragraphs to extract the most important sentences for summarization. The PageRank algorithm is applied to construct a rank representation of the text, with sentences as nodes and their co-occurrence patterns as edges. By calculating PageRank scores iteratively, the algorithm identifies the most central and significant sentences in the paragraph. Linguistic features such as sentence length, position, and content relevance are incorporated to enhance the quality of the summaries. Additionally, the algorithm dynamically adjusts sentence importance based on their relations with other sentences rank. Experimental results on a comprehensive dataset demonstrate that the PageRank-based approach outperforms existing summarization techniques in terms of informativeness and coherence, as evidenced by higher similarity score. The proposed method offers an effective solution for optimizing Hindi paragraph summarization, facilitating the management and comprehension of Hindi text in various applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Development of Autonomous Robots for Industrial and Domestic Applications

Development of Autonomous Robots for Industrial and Domestic...

引用

International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： P. Ramadevi VijayaBhaskar Sadu Veeresh Fuskele Siva Nagaiah Bolleddu T. Ch. Anil Kumar M. Kalyan Chakravarthi Department of Computer Science Engineering (Cyber Security) Institute of Aeronautical Engineering Hyderabad Telangana India Department of Mechanical Engineering Jawaharlal Nehru Technological University Kakinada Andhra Pradesh India Department of Mechanical Engineering BabulalTarabai Institute of Research and Technology Sironja Madhya Pradesh India Associate Professor of English and Coordinator KL-CDOE Koneru Lakshmaiah Education Foundation - Deemed to be University Guntur Andhra Pradesh India Department of Mechanical Engineering Vignan's Foundation for Science Technology and Research Guntur Andhra Pradesh India School of Electronics Engineering VIT-AP University Amaravathi Andhra Pradesh India

This research study investigates the potential design of autonomous robots for developing the domestic and industrial applications. The system underwent rigorous testing in both commercial and residential settings, demonstrating exceptional performance in navigation and achieving near-perfect scores in manipulation. Autonomous robots possess the capacity to enhance productivity, reduce expenses, and enhance safety in various scenarios, as emphasized in the literature. The report also outlines potential avenues for future research, which encompass the development of enhanced sensors and perceptual systems, collaborative systems, and advanced decision-making algorithms. In general, this study constitutes a crucial contribution to the expanding corpus of information pertaining to self-governing robots and will facilitate their prospective conception, advancement, and integration.

关键词：

来源：评论

学校读者我要写书评

暂无评论

RETRACTED ARTICLE: ALBAE feature extraction based lung pneumonia and cancer classification

引用

Soft Computing 2023年第2期28卷 589-589页

作者： M Braveen S Nachiyappan R Seetha K Anusha A Ahilan A Prasanth A Jeyam Assistant professor senior School of Computer Science and Engineering Vellore institute of technology Chennai Tamil Nadu India Associate Professor School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Associate Professor School of Information Technology and Engineering Vellore Institute of Technology Vellore Tamil Nadu India Associate Professor Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India Assistant Professor Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering Sriperumbudur India Assistant Professor Computer Science and Engineering Lord Jegannath College of Engineering and Technology Kanyakumari Tamil Nadu 629402 India

来源：评论

学校读者我要写书评

暂无评论

Advanced skin lesion segmentation and classification using adaptive contextual GLCM and deep learning hybrid models

引用

Egyptian Informatics Journal 2025年 30卷

作者： Vinit Kumar D.L. Shanthi Tummala Ranga Babu Narendra Kumar Rakesh Kumar Godi Dr Arun G Professor Galgotias College of Engineering and Technology Greater Noida India Assistant Professor Department of Computer Science and Engineering BMS Institute of Technology and Management Bangalore Karnataka India Professor Electronics & Communication Engineering R.V.R. & J.C.College of Engineering India Associate Professor Department of CSE Amity University Jharkhand Ranchi Jharkhand India Department of Computer Science Central University of Karnataka Kalaburagi 585367 India Assistant Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India

One of the most common and dangerous types of cancer, skin cancer, especially melanoma, is distinguished by its quick spread and high death rate if left untreated. It is impossible to overestimate the significance of early detection and precise diagnosis as they are essential for successful treatment and greatly raise patient survival rates. To improve skin lesion segmentation and classification, this work presents a state-of-the-art image analysis technique that uses hybrid deep learning models in conjunction with the Adaptive Contextual Gray Level Co-occurrence Matrix (GLCM). DeepLabV3 + is used to segment the skin lesion from pre-processed input images with high accuracy. A Long Short-Term Memory (LSTM) network trained on characteristics collected by the Adaptive Contextual GLCM is included to further evaluate the segmented images. Because of this special combination, the model can accurately capture contextual information and tiny texture differences, both of which are essential for differentiating between benign and malignant skin lesions. Our suggested approach performs very well in tasks involving both segmentation and classification. In particular, it attains a remarkable 98.34 % accuracy, 99.13 % specificity, 97.25 % sensitivity, 97.15 % Dice coefficient, and 98.6 % F1 score. These findings demonstrate the method’s stability and dependability, which makes it a potentially useful instrument for raising the detection accuracy of skin cancer. This approach has the potential to transform early detection procedures by providing improved diagnostic accuracy and resilience, eventually contributing to better patient outcomes and more effective management of skin cancer cases.

关键词： Skin cancer Machine Learning Deeplabv3 Adaptive Contextual Gray Level Co-occurrence Matrix Long Short-Term Memory

来源：评论

学校读者我要写书评

暂无评论

IoT-based shrewd crop field MPA system

引用

AIP Conference Proceedings 2025年第1期3224卷

作者： Himanshu Agarwal Shweta Agarwal Utkarsh Mishra Vanshika Chaudhary Vanshita Singh Shaurya Chauhan Associate Professor Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad (U.P.)-India Assistant Professor Department of Mathematics Ramabai Ambedkar Government Degree College Gajraula Amroha (U.P.)-India UG Scholars Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad (U.P.)-India

Context: Agriculture stands as a pivotal driver of economic progress within a nation, yet the realm of technical advancements within this sector remains distressingly neglected by a multitude of governments. While farmers contribute untiring efforts to tend to their fields, substantial time is squandered on tasks like irrigation and safeguarding crops from birds and animal threats [1, 2]. This unwavering dedication often exacts a toll on farmers’ health, leading to ailments and respiratory issues stemming from exposure to noxious gases emitted by certain crops. Extensive research endeavours [3-9] have been undertaken to alleviate the burdens faced by farmers. These efforts, however, frequently culminate in singular applications such as automated irrigation systems or electric perimeters for crop protection. A subset of researchers has also delved into probing the prevalence of harmful gases across agricultural fields. This paper proposes an innovative approach to address these challenges through the utilization of Internet of Things (IoT) technology. Objective: The proposed solution canters on a NodeMCU powered intelligent crop field Monitoring-Protection-Alert (MPA) system, which serves as a technical path to revolutionize farming practices. The core objective underpinning the proposed system is the acquisition of real-time insights emanating from the crop field. This critical initiative empowers farmers with timely and accurate data, enabling them to make informed and precise decisions pertaining to their agricultural domain. Methods: By seamlessly integrating various sensors, the system detects the presence of birds, animals, and noxious gases in real-time. Furthermore, it enhances crop productivity by continuously monitoring soil parameters, including temperature and moisture levels, thereby optimizing irrigation processes. For seamless communication, the system is fortified with a GSM module that promptly alerts farmers about potential threats to their crops.

关键词：

来源：评论

学校读者我要写书评

暂无评论

IoT Research on Healthcare Data to Control Illness of Oncology

IoT Research on Healthcare Data to Control Illness of Oncolo...

引用

Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Dhiraj Kapila Prince Tehseen Ganai G.Sreenivasa Raju Lims Thomas Manisha Girase José Luis Arias Gonzales Assistant Professor Department of Computer Science &#x0026 Engineering Lovely Professional University Phagwara India Research Scholar Department of Information Technology Shri Jagdishprasad Jhabarmal Tibrewala University Rajasthan India Assistant Professor Department of Electronics and Communication Engineering Anurag University Assistant Professor Department of Social Work Vimala College (Autonomous) Thrissur Professor &#x0026 Head Dept. Of Microbiology K SK W Arts Science &#x0026 Commerce College CIDCO Nashik Maharashtra Departrnent of business University of British Columbia

Let us begin with the internet of things that empowers software programmers to start increasing prediction without being done with full to do so. With today's data availability, machine learning techniques are being developed to improve performance and maintenance prediction. Cancer, according the Health Organisation, is a global disease and the second leading cause of death worldwide. People with cancer have been identified as having an increased risk of dying in the current worldwide epidemic. With the disease's incidence growing decade after decade, the need for a continuously and then round surveillance apparatus has become crucial. A cancer monitoring system known as the internet of Things (IoT) has the potential to identify cancer-related indications in their initial phases, continue to monitor disease individuals, and screen someone who has been cured through post-treatment operations. This research presents a multi-layered architectural for an IoT-based disease monitoring and control system that might be utilised as a foundation for remotely cancer diagnosis and management.

关键词： Wireless communication Hospitals Surveillance Machine learning Maintenance engineering Oncology Software

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：