检索结果-内蒙古大学图书馆

8th International Conference on ICT for Smart Society, ICISS 2021

作者： Kusumawardani, Dwi Mustika Saintika, Yudha Romadlon, Fauzan Information System Study Program Faculty of Informatics Institut Teknologi Telkom Purwokerto Purwokerto Indonesia Industrial Engineering Study Program Faculty of Industrial Engineering and Design Institut Teknologi Telkom Purwokerto Purwokerto Indonesia

ISBN: (纸本)9781665416979

The rapid development of technology presents a new paradigm, one of which is in the transportation sector. The concept of smart mobility is one of the components in the realization of a smart city, which is closely related to the transportation sector. The Purwokerto-Purbalingga Bus Rapid Transit (BRT) is the government's effort to encourage the transportation sector towards smart mobility. It requires three crucial categories: Accessibility, sustainability, and communication and information technology. This study proves and explains that these three categories can be fulfilled by the Purwokerto-Purbalingga Bus Rapid Transit, especially from women's perception, indicated by an average accessibility score of 4.04, sustainability of 4.22, and Information and Communication Technology of 3.80. Efforts that can be taken by the government and Bus Rapid Transit managers include monitoring and evaluating bus stops and bus arrival frequencies as well as developing mobile-based applications that provide real-time information related to bus rapid transit. © 2021 IEEE.

关键词： Sustainable development

来源：评论

学校读者我要写书评

暂无评论

Seafloor Imaging Maps by Incorporating Side Scan Sonar Technology on Hybrid Autonomous Underwater Glider

Seafloor Imaging Maps by Incorporating Side Scan Sonar Techn...

引用

International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Ardian Budi Kusuma Atmaja Egi Muhammad Idris Hidayat Bambang Riyanto Trilaksono Simon Siregar Natsir Habibullah School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Department Computer Engineering Faculty of Applied Science Doctoral Program of Electrical Engineering Institut Teknologi Bandung Telkom University

This research aims to construct a two-dimensional image to represent an underwater geometry map with a Side Scan Sonar (SSS) mounted on a Hybrid Autonomous Underwater Glider (HAUG). Building the underwater map has two stages of the process. The first stage is preprocessing which includes Time-varying gains (TVG), slant range correction, and ground range correction. The second is HAUG navigation, the movement of HAUG orientation to the angle and distance of SSS readings and plotting the geometry of the grid map. With these two stages, a two-dimensional grid map will be formed. Meanwhile, the 3D plot utilizes slant range and ground range correction to obtain the surface height of the bottom of the pool. From the experiments conducted to conduct mapping in the pool, two-dimensional and three-dimensional visual forms of the pool floor and pool walls were produced. The results of the estimated measurement of the width of the pool obtained 14.05 meters and the height of the pool 3.199 meters with an SSS beam angle of 58 degrees.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Mapping the Literature of Artificial Intelligence in Medical Education: A Scientometric Analysis 7th

Mapping the Literature of Artificial Intelligence in Medical...

引用

7th International Conference on Inventive Communication and Computaional Technologies, ICICCT 2023

作者： Maulana, Fairuz Iqbal Zaın, Muhammad Yasır Lestari, Dian Purnomo, Agung Adi, Puput Dani Prasetyo Computer Science Department Bina Nusantara University Jakarta11480 Indonesia Informatics Engineering Study Program University of Madura Pamekasan Indonesia Faculty of Medicine Universitas Airlangga Surabaya60132 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Jakarta11480 Indonesia Telecommunication Research Center National Research and Innovation Agency Jakarta11480 Indonesia

ISBN: (纸本)9789819951659

This study aims to investigate the use of artificial intelligence in health education in the last ten years from 2012 to 2022 using the Scopus database. Researchers use bibliometric analysis combined with the quantification method of Vosviewer and Rstudio software used for literature analysis. The results of the article that were flushed showed data such as the year of publication, journal, country, keywords, and authors, to the highest number of citations. The general keywords used by researchers are artificial intelligence, medical, and education. Researchers limit findings through keywords as in the last ten years and English only so that 1681 articles were obtained. According to the study's findings, McGill University affiliation had the greatest number of papers, with 69 pieces, and medicine had a proportion of 39.7% (n = 1067). This bibliometric study will be useful for other researchers to examine the development of research on artificial intelligence in health education in the last ten years. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Medical education

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Effectiveness of Smartphone Applications on Disease Management and Healthcare Systems

Analyzing the Effectiveness of Smartphone Applications on Di...

引用

International Conference on Application of Information and Communication Technologies (AICT)

作者： Rabei Raad Ali Wisam Subhi Al-Dayyeni Christy Atika Sari Eko Hari Rachmawanto Candra Irawan Omar M. Jihad Department of Computer Engineering Technology Northern Technical University Mosul Iraq School of Information Technologies and Engineering ADA University Baku Azerbaijan Study Program in Informatics Engineering Faculty of Computer Science University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems Faculty of Computer Science University of Dian Nuswantoro Semarang Indonesia

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

In today’s complex global environments, it is highly challenging to achieve effective healthcare management. The critical challenges faced by healthcare management are being met by the revolution of m-health where mobile cloud computing plays a major role. The taxonomy of mobile computing comprises operational aspects, end-user issues, and service quality and mobility management. The use of smartphone technologies and applications has become a highly significant approach to improving healthcare management. Mobile health services such as mobile pathology, mobile neurosurgery, cancer treatment, and behavioral/psychological disorders are gaining significance where smartphone applications are being used. Portability, flexibility, and convenience are major characteristics of mobile computing that have helped patients and doctors to develop better relationships through coordination and communication. The relationship between smartphone technologies and applications and healthcare management can be understood in several broad aspects. This article aims to analyze the current and future implications of these technologies on healthcare and disease management systems.

关键词： Pathology Cloud computing Taxonomy Cancer treatment Medical services Benchmark testing Neurosurgery Information and communication technology Mobile computing Diseases

来源：评论

学校读者我要写书评

暂无评论

Deepepiil13: Deep Learning for Prediction of Il-13 Inducing Epitopes Using Pre-Trained Language Models and Multi-Window Convolutional Neural Networks

SSRN

引用

SSRN 2024年

作者： Chuang, Cheng-Che Ou, Yu-Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan

Interleukin-13 (IL-13) is a key cytokine involved in allergic inflammation and the cytokine storm associated with severe COVID-19. Identifying antigenic epitopes capable of inducing IL-13 holds therapeutic potential for developing epitope-based vaccines and immunomodulatory treatments. However, conventional methods for epitope prediction are often inefficient and inaccurate. This study presents DeepEpiIL13, a novel deep learning framework that integrates pre-trained language models and multi-window convolutional neural networks (CNNs) to rapidly and accurately predict IL-13-inducing epitopes from protein sequences. The ProtTrans pre-trained language model was employed to generate high-dimensional embeddings that capture rich contextual information from protein sequences. These embeddings were then inputted into a multi-window CNN architecture, enabling the efficient exploration of local and global sequence patterns associated with IL-13 induction. The proposed approach was rigorously evaluated using benchmark datasets and an independent SARS-CoV-2 dataset. DeepEpiIL13 achieved superior performance compared to traditional methods, with an impressive Matthews correlation coefficient (MCC) of 0.52 and an area under the receiver operating characteristic curve (AUC) of 0.86 on the benchmark dataset. On the independent SARS-CoV-2 dataset, DeepEpiIL13 demonstrated remarkable robustness, attaining an MCC of 0.63 and an AUC of 0.92, highlighting its potential applicability in the context of COVID-19 research. This study introduces a powerful deep learning framework for accurate epitope prediction, offering new avenues for the development of epitope-based vaccines and immunotherapies targeting IL-13-mediated disorders. The successful identification of IL-13-inducing epitopes paves the way for novel therapeutic interventions against allergic diseases, inflammatory conditions, and potentially severe viral infections like COVID-19. © 2024, The Authors. All rights reserved.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Prototype Design of Smart Diabetic Shoes with Lora Module Communication

Prototype Design of Smart Diabetic Shoes with Lora Module Co...

引用

2023 International Conference on Computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Purwono, Purwono Burhan, Asmat Nisa, Khoirun Wibisono, Sony Kartika Setiawan Mangkunegara, Iis Dewi, Pramesti Ma'Arif, Alfian Suwarno, Iswanto Universitas Harapan Bangsa Department of Informatics Purwokerto Indonesia Universitas Harapan Bangsa Department of Anesthesiology Nursing Purwokerto Indonesia Universitas Harapan Bangsa Department of Information Technology Purwokerto Indonesia Universitas Harapan Bangsa Nursing Study Program Purwokerto Indonesia Universitas Ahmad Dahlan Department of Electrical Engineering Yogyakarta Indonesia Universitas Muhammadiyah Yogyakarta Department of Engineer Professional Program Yogyakarta Indonesia

ISBN: (纸本)9798350320954

Diabetes is a disease that affects many people in the world. Diabetes mellitus is a type of metabolic disease in a person who suffers from blood glucose levels with extreme conditions, namely insufficient insulin production in the human body. Monitoring diabetes is an important concern for researchers because it can be useful for improving the quality of the nursing service system. One of the common conditions in diabetic patients is ulceration which is difficult to detect on time. Technology can minimize the total cost of monitoring chronic diseases such as Diabetes continuously and on time. This research focuses on solutions to produce IoT-based smart diabetic shoes that utilize pressure sensors and the temperature of the feet of people with diabetic feet. Smart diabetic shoes are made using the Lora E32 module to be applied to areas with poor internet connections. The results of the testing carried out for 60 seconds in this study succeeded in detecting the area of the foot that experienced the greatest pressure, which was located on the rear footrest with a portion of pressure of around 25 - 28% of the total body weight. The patient's foot temperature increases when the pedestal load is greater. The Lora E32 module also functions as a media transmitter and receiver at a distance of 2.2 km in sending sensor data. © 2023 IEEE.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Towards Precision Cancer Therapy: Prottrans and Multi-Window Scanning Convolutional Neural Networks Transforming Protein-Peptide Interaction Predictions

SSRN

引用

SSRN 2024年

作者： Van Le, The Zi Zhan, Jun Yu Ou, Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan

This study delves into the prediction of protein-peptide interactions using advanced machine learning techniques, comparing models such as sequence-based, standard CNNs, and traditional classifiers. Leveraging pre-trained language models and multi-view window scanning CNNs, our approach yields significant improvements, with ProtTrans standing out based on 2.1 billion protein sequences and 393 billion amino acids. The integrated model achieves an impressive AUC of 0.855 and 0.822 on Set_1 and Set_2 datasets, outperforming previous methods. Beyond this, we explore the application of this model in cancer therapy, particularly in identifying peptide interactions for selective targeting of cancer cells. The findings of this study contribute to bioinformatics, providing valuable insights for drug discovery and therapeutic development. © 2024, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Analysis of Mask Detection Using Convolutional Neural Networks (CNN) and Single Shot Multibox Detector (SSD) Approach

A Comprehensive Analysis of Mask Detection Using Convolution...

引用

2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Mukti, Bayu Krisna Novita, Mega Amirudin, Muhammad Singh, Sanjay Kumar Harjanto, Imadudin Bhardwaj, Priyanka Kusmantoro, Adhi Roy, Manidipa Universitas Pgri Semarang Faculty of Engineering and Informatics Department of Electrical Engineering Jl. Sidodadi Timur No. 24 Central Java Semarang50232 Indonesia Universitas Pgri Semarang Postgraduate Program of Science Education Jl. Sidodadi Timur No. 24 Central Java Semarang50232 Indonesia ABES Engineering College Department of Electronics & Communication Engineering 19th KM Stone NH-09 Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9798350329186

The Covid-19 pandemic has emphasized the critical role of mask-wearing in reducing virus transmission. However, declining public awareness necessitates effective monitoring solutions. This study explores the Single Shot Multibox Detector (SSD) approach, a Convolutional Neural Networks (CNN)-based object detection method, for mask detection. Specifically, we investigate the performance of the SSD model with the ResNet50 architecture as the base network. Through the analysis of a dataset comprising 1000 images of individuals wearing and not wearing masks, the SSD model with ResNet50 achieves an impressive accuracy of 98.23%, surpassing the VGG16-based model's accuracy of 93.56%. These findings underscore the superiority of the CNN-based SSD model with ResNet50 for mask detection. Moreover, they have significant implications for real-time monitoring and enforcing mask-wearing protocols, emphasizing the importance of public awareness and adherence. Leveraging advanced AI techniques, such as CNN-based object detection with SSD, strengthens public health initiatives. The development of robust and efficient mask detection systems contributes to creating a safer environment amid the ongoing Covid-19 pandemic. This research highlights the value of harnessing these advanced technologies to effectively address public health crises. By employing CNN-based SSD with ResNet50, society can enhance its ability to mitigate risks and ensure public safety. © 2023 IEEE.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Images 5

Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Im...

引用

5th International Conference on engineering Technology and its Applications, IICETA 2022

作者： Alslatie, Mohammad Alquran, Hiam Mustafa, Wan Azani Abu-Qasmieh, Isam Alqudah, Ali Mohammad Alkhayyat, Ahmed The Institute of Biomedical Technology King Hussein Medical Center Royal Jordanian Medical Service Amman Jordan Yarmouk University Biomedical Systems and Medical Informatics Engineering Irbid Jordan Arau Faculty of Electrical Engineering Technology Perlis Malaysia University of Manitoba Biomedical Engineering Program Department of Electrical and Computer Engineering Canada The Islamic University Faculty of Engineering Najaf Iraq

ISBN: (纸本)9781665472159

The state of the art of artificial intelligence (AI) for various medical imaging applications leads to enhanced accuracy, analysis, visualization, and interpretation of chest Xray (CXR) images for diagnosis. Many diseases are diagnosed based on CXR images. In this paper, two types of abnormalities are diagnosed based on AI techniques. The two classes are atelectasis and cardiomegaly. The acquired images are segmented to localize the chest region and then enhanced using gray-level transformation methods. The enhanced images are passed to two pretrained convolutional neural networks (CNNs): shuffle and mobile net. The transfer learning approach is utilized in this stage. The automated features are extracted from the last fully connected layer. Each CNN deserves to have the two most representative features for the two classes. These four features are passed to support the vector machine classifier. The training accuracy reached 100% and the test accuracy was 96.7%. The proposed method can be extended to be a milestone in the classification of all heart-lung diseases that can be diagnosed using chest X-ray images. © 2022 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Design and Development of a Mobile Goods Transfer Robot Using Waypoint GPS Navigation Method

Design and Development of a Mobile Goods Transfer Robot Usin...

引用

2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Wiliyanto, Danang Puji Supriyadi, Slamet Mukhtar, Agus Setyoadi, Yuris Novita, Mega Dixit, Ankur Singh, Prabhat Virmani, Rahul Universitas Pgri Semarang Faculty of Engineering and Informatics Department of Mechanical Engineering Jl. Sidodadi Timur No. 24 Central Java Semarang50232 Indonesia Universitas Pgri Semarang Postgraduate Program of Science Education Jl. Sidodadi Timur No. 24 Central Java Semarang50232 Indonesia ABES Engineering College Department of Mechanical Engineering 19th KM Stone NH-09 Uttar Pradesh Ghaziabad201009 India

ISBN: (纸本)9798350329186

The use of automated goods transfer robots, capable of autonomously moving items from one point to another without the need for manual operation, is widely adopted in various industries. The Waypoint GPS navigation method can be employed in developing an automated goods transfer robot. This research focuses on the design and development of a robot using two Global Positioning System (GPS) coordinates for navigation and a smartphone as the user interface, equipped with ultrasonic sensors as input. The main components utilized in this robot include the Arduino Mega 2560 microcontroller, neo-7M GPS module, HC-06 Bluetooth module, SR-04 sensor, and omni-directional wheels. The position data is obtained by converting the two GPS coordinates into distance and direction values. These values are further translated into Pulse Width Modulation (PWM) signals, which are used to drive the robot's motors. To avoid collision, fuzzy logic is employed to regulate the PWM signals. The research results demonstrate that the robot can autonomously navigate from one point to another based on the provided coordinates while effectively avoiding obstacles. The findings of this study address a research gap in the application of the Waypoint GPS navigation method for goods transfer robots. The purpose of this research is to design and develop a robot that can autonomously transfer goods using the proposed method. The methodology involves the integration of GPS coordinates, smartphone interface, ultrasonic sensors, and motor control mechanisms. The results indicate successful navigation from the origin to the desired destination, following the specified coordinates, and obstacle avoidance capability. In conclusion, the developed robot utilizing the Waypoint GPS navigation method showcases promising potential for automated goods transfer applications. The ability to operate autonomously, guided by GPS coordinates, and the avoidance of obstacles enhances the efficiency and safety of goods transf

关键词： Navigation

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：