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2023 ieee International Conference on Innovative data Communication Technologies and Application, ICIDCA 2023

作者： Pande, Shilpa Mangesh Cmr Institute of Technology Department of Information Science and Engineering Bengaluru India Visvesvaraya Technological University Belagavi India

ISBN: (纸本)9798350397208

Education is the key to success which provides multiple opportunities in the life. Education not only contributes to individual success but also contributes to the success of the society. The Indian education system still follows the traditional way of the teaching learning process which lacks in interactive sessions. So, it becomes very difficult to continuously monitor student performance. Indian population is huge which makes the monitoring and analyzing student performance challenging. The Indian education system lacks in the standards for accessing the student performance and achievements. The proper methodology is required to monitor student's academic progress and development. Multiple parameters influence a student performance so it is a very challenging task to find out the significant parameters which are affecting a student performance. Another challenge in the education system is to identify the slow learners at an early stage. In this research work, various factors affecting student performance are analyzed and visualized like student's age, parent's education & occupation, health etc. The visualization technique is used to identify the weak students at an early stage to work on their improvement. To forecast the student performance, a variety of Machine Learning (ML) methods, including K Nearest Neighbors (KNN), Logistic Regression, and Support Vector Machine (SVM), are used. The SVM model with linear kernel gave the best accuracy 84.37%for the selected dataset. © 2023 ieee.

关键词： Support vector machines

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Neural Implicit Representation for Three-dimensional Ultrasound Carotid Surface Reconstruction using Unsigned Distance Function

Neural Implicit Representation for Three-dimensional Ultraso...

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2023 ieee International Ultrasonics Symposium, IUS 2023

作者： Chen, Hongbo Kumaralingam, Logiraj Li, Jiawen Punithakumar, Kumaradevan Le, Lawrence H. Zheng, Rui University of Alberta Department of Radiology and Diagnostic Imaging EdmontonAB Canada ShanghaiTech University School of Information Science and Technology Shanghai China University of Alberta Department of Biomedical Engineering EdmontonAB Canada Shanghai Engineering Research Center of Energy Efficient and Custom Ai Ic Shanghai China

ISBN: (纸本)9798350346459

Accurate 3D geometric shapes of carotid arteries are imperative for the three-dimensional (3D) ultrasound (US) imaging to clinically assess the carotid atherosclerosis (CA). However, the traditional surface reconstruction method, such as iso-surface (ISO-SURF), suffers from the image noise, voxel resolution and additional processing. In this paper, we introduce the neural implicit representation based on the deep learning network for the 3D surface reconstruction of media-adventitia boundary (MAB), plaque, and lumen-intima boundary (LIB) together. The unsigned distance functions are learned by the network to generate the mesh. For the validation, six volumes were simulated in carotid shape with MAB and LIB, and random noise was added around the boundaries. The results showed that experiments on six simulated volumes illustrated better performance than ISO-SURF, with 47%, 36%, and 55% decrease in Chamfer distance, average absolute distance, and Hausdorff distance, respectively. The visualization results from the CA clinical data revealed a smoother and more continuous geometric surface than ISO-SURF. The comparison result has shown the potential of the proposed method to examine vascular pathologies in the future. © 2023 ieee.

关键词： Diseases

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Cirrhosis Prediction in Chronic Liver Disease Patients Using Machine Learning Techniques 3

Cirrhosis Prediction in Chronic Liver Disease Patients Using...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Athish, V.P. Sree Nandha, S.S. Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Dept. of Data Science and Business Systems Tamilnadu Kattankulathur603203 India

ISBN: (纸本)9798350322842

Liver failure is a serious medical condition that can have life-threatening consequences. The liver is responsible for filtering blood and performing many other important functions. With the rise of machine learning in healthcare, there is an opportunity to use past datasets to predict liver failure early on. The main focus of this research work is to explore the application of supervised machine learning algorithms in predicting liver failure. The study involved pre-processing techniques such as univariate and bivariate analysis, and data visualization to gain better insights into the features of the dataset. Using these techniques, a multi-class classification model was built with machine learning algorithms, and the performance metrics of accuracy, F1 score, and recall were used to compare the algorithms. The results show that machine learning algorithms can accurately predict liver failure and can be an effective tool in healthcare. This study demonstrates the potential of machine learning in predicting liver failure and can contribute to the development of future models in the field. The results of this research study indicates that the proposed algorithm achieved an accuracy of 94.48%. The proposed work will be beneficial to researchers in this field and inspire them to build more advanced and accurate models. © 2023 ieee.

关键词： Support vector machines

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Design and Development of Cloud and IoT-Based Monitoring System for Environmental Health and Safety in Homes

Design and Development of Cloud and IoT-Based Monitoring Sys...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Bura Vijay Baby Rani, N. Krishna Prakash, R. Adusumilli, Manasa Sunil Kumar, V. Sr University School of Computer Science and Artificial Intelligence Warangal India Malla Reddy Engineering College for Women Dhulapally It Department Hyderabad India Sona College of Technology Department of Information Technology Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Yelahanka Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Residential pollution is portrayed as a key contributor to health problems and environmental damage since it comes from indoor and outdoor sources. To discover a solution, a monitoring system should be built and capable of measuring the level of pollutants in typical residential environments. The suggested method makes use of cloud computing and the Internet of Things to measure pollution in residential areas. The goal is to monitor indoor air quality, gather data, and then communicate those findings with users in real time through cloud storage. An STM32 CPU, an ESP8266 wireless LAN module, a multisensory and hazardous substances are used in suggested system. According to the findings, the error rates for PM 2.5 and noise are 6-7% and 1%, respectively, while the error rate for readings of hazardous gases is 6%. The sensors are updated in 0.76 seconds, and an alarm results in a response of 2 seconds. © 2023 ieee.

关键词： Cloud Computing data analysis Internet of Things Monitoring system visualization Wireless communication technologies

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Hidden Gold for IT Professionals, Educators, and Students: Insights From Stack Overflow Survey

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ieee TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第2期10卷 795-806页

作者： Dada, Oluwaseun Alexander Obaido, George Sanusi, Ismaila Temitayo Aruleba, Kehinde Yunusa, Abdullahi Abubakar Univ Helsinki Inst Mol Med Finland FIMM Helsinki 00290 Finland Sch Software Lagos 105101 Nigeria Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA Univ Eastern Finland Sch Comp Joensuu 80101 Finland Walter Sisulu Univ Dept Informat Technol ZA-5099 East London South Africa Univ Sains Malaysia Ctr Instruct Technol & Multimedia George Town 11800 Malaysia Usmanu Danfodiyo Univ Sokoto Dept Curriculum Studies & Educ Technol Sokoto 840104 Nigeria

It is undeniable that technological advancements have taken significant changes in the nature of work, resulting in the loss of many jobs and the emergence of new ones. As a result, information technology (IT) job seekers and professionals must be aware of changes required in their respective fields. This study identified and ranked the most-used technologies, such as programming languages, databases, operating systems (OSs), and collaborative tools, specific to twenty-three (23) different IT roles. Furthermore, this study advocates that individuals focus on critical skills required in various domains to achieve career success. Similarly, individuals with a set of valued technical skills may decide whether to change jobs or roles if they are interested in a position that requires their current competence. The findings of this study can be helpful to career counselors, instructors, students, professionals, recruiters, curriculum designers, and decision-makers.

关键词： Software Industries Employment Engineering profession databases Hardware visualization Collaborating tools data science GitHub information technology (IT) IT job roles programming languages software developer Stack Overflow

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A Scientometric Analysis of the Capacity Expansion and Planning Research

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ieee TRANSACTIONS ON ENGINEERING MANAGEMENT 2023年 71卷 6382页

作者： AlDurgam, Mohammad M. Tuffaha, Firas M. Abdel-Aal, Mohammad A. M. Almoghathawi, Yasser Saleh, Haitham H. Saleh, Zead King Fahd Univ Petr & Minerals Dept Ind & Syst Engn Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Smart Mobil & Logist Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Dept Construct Engn & Management Dhahran 31261 Saudi Arabia

Numerous technoeconomic factors have had a considerable impact on the industrial and service sectors. The best uses of production and service resources have been a main research area. Capacity planning, which aims at the optimum utilization of resources, has attracted a lot of scholarly attention. To the best of our knowledge, however, there has never been a scientometric analysis published on this subject;as a result, the goal of this article is to provide the first such analysis. The goal of this study is to gain a thorough structural view of the background and important facets of capacity expansion and planning research. The Web of science database, VOSviewer, and CiteSpace software are used to give an overview. Our research indicates that after the year 2000, the publication rate increased significantly. Applications for capacity planning can be divided into eight main groups based on the authors' keyword analysis. We also list both well-known and underutilized modeling and solution methods. Recent applications, research trends, and some omissions are highlighted in this study. Leading contributing countries, institutions, and the most common research outlets are mapped and analyzed. Finally, some potential future research topics are suggested

关键词： Capacity planning Bibliometrics Planning data visualization Market research databases Surveys Bibliometric analysis capacity expansion Index Terms capacity planning scientometric analysis

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Adaptive Bias-Aware Feature Generation for Generalized Zero-Shot Learning

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ieee TRANSACTIONS ON MULTIMEDIA 2023年 25卷 280-290页

作者： Yang, Yanhua Zhang, Xiaozhe Yang, Muli Deng, Cheng Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

Zero-Shot Learning (ZSL) aims to recognize unseen classes that never appear during training. Recently, generative adversarial networks (GANs) have been introduced to convert ZSL into a supervised learning problem by synthesizing unseen visual features. However, since unseen classes are never experienced for the generator during training, the synthesized unseen visual features often become heavily biased towards seen classes, or sometimes there is even no meaningful class that can be assigned to them. This is known as the bias problem. In this paper, we propose a novel method, namely Adaptive Bias-Aware GAN (ABA-GAN), to alleviate generating biased visual features. For this purpose, we build a semantic adversarial network to regularize the feature generator. Specifically, an adaptive adversarial loss is proposed to constrain the feature distributions, which avoids the generation of meaningless visual features. Meanwhile, a domain divider is presented to explicitly distinguish synthesized visual features between seen and unseen domains, such that the bias towards seen classes can be alleviated. Moreover, we propose a novel metric named bias score (BS) to explicitly quantify the degree of the strong bias. Extensive experiments on four widely used benchmark datasets demonstrate that our proposed method outperforms the state-of-the-art approaches under both ZSL and GZSL protocols.

关键词： visualization Semantics Generators Training Generative adversarial networks data models Benchmark testing Zero-shot learning generative adversarial network image classification bias problem

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EAD-YOLO: Improved YOLOv5 for Endoscopic Artefact Detection

EAD-YOLO: Improved YOLOv5 for Endoscopic Artefact Detection

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2023 International Conference on Communications, Computing and Artificial Intelligence, CCCAI 2023

作者： Yuan, Zhaoyu Ye, Jing Qian, Cheng Li, Xiaoqiong Beijing Institute of Technology School of Medical Technoloy Beijing China Beijing Institute of Technology School of Life Science Beijing China

ISBN: (纸本)9798350311297

Endoscopic artefacts seriously affect the visualization of the lesion area during endoscopy and the results of automated detection and analysis. Therefore, accurate detection of clinical endoscopic artefacts has become a key bottleneck. In this paper, aiming at the problems of data imbalance and severe multi-scale distribution of objects in the endoscope artefact dataset, we propose a multi-scale detection algorithm EADYOLO. Based on the baseline network YOLOv5, the algorithm has been improved in three aspects. First, the data imbalance is alleviated by adopting data augmentation methods. Second, by constructing a multi-scale detection layer to improve the detection effect of the algorithm on endoscopic artefact targets of different scales. The depthwise separable convolution is used to reduce the number of parameters of the network and improve the feature extraction capability of the backbone network. Finally, Atrous Spatial Pyramid Pooling (ASPP) module is used to further enhance the expression ability of multi-scale target features. Compared with the baseline network, EAD-YOLO can improve the detection accuracy and recall rate of small and large targets by 3.23%, 7.64%, 2.37%, and 4.45% respectively. The comparison with classical and effective algorithms further proves that EAD-YOLO can better adapt to the severe size changes of endoscopic artefacts, and is more beneficial to the task of endoscopic artefact target detection. © 2023 ieee.

关键词： Endoscopy

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A MULTI-STREAM NETWORK FOR MESH DENOISING VIA GRAPH NEURAL NETWORKS WITH GAUSSIAN CURVATURE 30

A MULTI-STREAM NETWORK FOR MESH DENOISING VIA GRAPH NEURAL N...

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30th ieee International Conference on Image Processing (ICIP)

作者： Zhao, Zhibo Wu, Wenhui Liu, Hongjie Gong, Yuanhao Shenzhen Univ Shenzhen Peoples R China Guangdong Key Lab Intelligent Informat Proc Shenzhen Peoples R China Reexen Technol Ltd Shenzhen Peoples R China Guangdong HK Joint Lab Big Data Imaging & Commun Shenzhen Peoples R China

ISBN: (纸本)9781728198354

3D meshes are getting popular in both research and industry. However, the meshes obtained via the 3D scanning equipment frequently contain a high level of noise. In this paper, we present a Gaussian Curvature Driven Multi-stream Network (GCM-Net) based on graph convolutional networks. This network can remove the noise while preserving the essential features during the 3D mesh denoising process. Our method is the first attempt to apply the high-order feature (i.e., Gaussian curvature) in the denoising task, which is more descriptive for the shape of the mesh. GCM-Net consists of curvature stream, vertex stream, and face normal stream, where the curvature stream focuses on the high-order Gaussian curvature feature of 3D mesh. Our method achieves state-of-the-art results on a publicly available dataset, demonstrating its effectiveness. The proposed method can be applied in various applications, such as 3D human body modeling, metaverse, object tracking and biomedical visualization.

关键词： Mesh denoising graph convolutional networks multi-stream Gaussian curvature

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A Deep Learning-Based Flight Turnaround Record System

A Deep Learning-Based Flight Turnaround Record System

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2023 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2023

作者： Ran, Jielong Wan, Hao Tang, Wenxiao Kang, Wenxiong School of Automation Science and Engineering South China University of Technology Guangzhou China Guangdong Airport Baiyun Information Technology Co. Ltd. Postdoctoral Innovation Practice Base Guangzhou China

ISBN: (纸本)9798350309003

To ensure the smooth progression of the flight turnaround, airports need to record the start and end times of each node in the flight turnaround process, compare them with the desired schedule, and guide the progress of subsequent flight tasks. Therefore, the timeliness and accuracy of data collection at each turnaround node become crucial. Traditionally, some airports manually record flight turnaround node times, which is inefficient and unable to realize data structuring and visualization. To address the above issues, we propose a flight turnaround record system based on object detection. The system builds upon the YOLOv5s-based detection network by introducing attention mechanisms with FasterNet as the backbone to enhance model performance and inference speed. Furthermore, we introduce dual-perspective coordination for aircraft positions, adaptive masking, and other methods to assist the system in making logical determinations about the status of nodes. Experiments have shown that our proposed detection network outperforms the baseline YOLOv5s method on our self-built apron dataset, with a 20fps inference speed increase and a 0.08 mAP@.5:.95 detection accuracy improvement. Compared to manual methods, our system reduces the average time error for each node by 60-120 seconds, significantly improving the efficiency of flight turnaround. © 2023 ieee.

关键词： Object detection

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