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检索条件"任意字段=2023 3rd International Conference on Detection Technology and Intelligence System, DTIS 2023"

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Artificial Neural Network-Driven Healthcare Resource Management for Improved Outcomes in Infectious Disease Control 3

Artificial Neural Network-Driven Healthcare Resource Managem...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Mothilal, T. Paranthaman, V. Socrates, S. Aluvala, Srinivas Kcg College of Technology Department of Automobile Engineering TN Chennai India Adhi College of Engineering and Technology Dept. of Mechanical Engineering TN Chennai India Velammal Institute of Technology Department of Mechanical Engineering TN Chennai India School of Computer Science & Artificial Intelligence Sr University Telangana Warangal India

ISBN: (纸本)9798350343274

The efficient administration of healthcare resources is crucial for successful infectious disease control. The merging of technology and medicine has opened the path for revolutionary changes in today's healthcare system. Artificial Neural Networks (ANNs) are one of them that has the potential to radically alter the way healthcare is administered. Using ANNs, this paper presents a novel method dubbed 'Dynamic Patient Risk Stratification (DyPRoN).' DyPRoN performs real-time risk assessments of individual patients, allowing for flexible distribution of medical resources. Adaptability to the dynamic nature of infectious illnesses is hindered by the prevalence of static models in conventional approaches. However, DyPRoN uses ANNs to do continuous analysis of patient data, allowing for risk classification in real time. This adaptive method improves healthcare resource management, which in turn helps with infectious disease control. © 2023 IEEE.

关键词： Neural networks

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Evaluation of Machine Learning Algorithms for Text Classification Tasks 3

Evaluation of Machine Learning Algorithms for Text Classific...

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3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Aravindan, M.K. Raajini, X. Mercilin Ingle, Yashwant S. Jangid, Pradeep Kumar Katyal, Ashu Naval, Preeti Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Vivekananda Global University Department of Electrical Engineering Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350319125

Assessing machine-learning algorithms for texttype responsibilities is critical to advancing the world of herbal language processing (NLP). As using applications that use NLP grows, so does the want to pick the maximum accurate algorithms out of a pool of to-be-had solutions. This paper reviews the current United States of America research concerning assessing system reading algorithms for textual content-type obligations. Numerous evaluation metrics, consisting of accuracy, precision, consider, and f-degree, are discussed, in addition to the strategies used to examine algorithms and determine the foremost solutions. The diverse challenges confronted in this venture are highlighted, giving readers an outline of the challenges related to assessing the tool and getting to know algorithms for text-kind responsibilities. © 2023 IEEE.

关键词： Natural language processing systems

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Intrusion Type Classifier: A Machine Learning Based Approach for Automated detection and Classification of Network Intrusions 3

Intrusion Type Classifier: A Machine Learning Based Approach...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kumar, Atul Sharma, Ishu Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

The increased complexity and frequency of network breaches present substantial obstacles in network system security maintenance. In order to tackle this issue, a proposed solution is the implementation of an Intrusion Type Classifier (ITC), which utilizes a machine learning methodology to identify and categorize network intrusions autonomously. The system employs a wide range of variables derived from network traffic data and employs multiple machine learning methods to effectively identify and classify various forms of intrusions. The primary objective of the ITC is to improve the effectiveness and precision of intrusion detection through the utilization of a multi-class classification framework. This framework is designed to differentiate between different types of intrusions, including Denial of Service (DoS), Distributed Denial of Service (DDoS), intrusions caused by malware, and other forms of network attacks. Through the utilization of extensive datasets, the ITC exhibits encouraging outcomes in the identification and classification of these intrusions, achieving notable levels of precision and recall rates. The presented methodology demonstrates a pragmatic and efficient strategy for enhancing the security of computer networks, facilitating prompt reactions to possible risks and weaknesses in intricate network settings. The support vector machine algorithm is widely recognized as one of the top-performing algorithms in various Network infrastructure domains. © 2023 IEEE.

关键词： Classification Cyber Threats Intrusion detection Machine Learning Naive Bayes Classifier Network Security Random Forest Security Support Vector Machine

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Real-Time Defect detection Monitoring system For Extrusion-based 3D Food Printing Using Internet of Things 3

Real-Time Defect Detection Monitoring System For Extrusion-b...

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3rd international conference on Smart Cities, Automation and Intelligent Computing systems, ICON-SONICS 2023

作者： Mawardi, Cholid Buono, Agus Priandana, Karlisa Herianto Institut Pertanian Bogor Department of Computer Science Bogor Indonesia Universitas Gadjah Mada Department of Mechanical and Industrial Engineering Yogyakarta Indonesia

ISBN: (纸本)9781509062805

Quality control remains a significant challenge in 3D food printing, in which defects during the printing process will lead to resource and time wastage. This work proposes a novel approach integrating cameras, image processing, and supervised machine learning to autonomously assess the quality of 3D food printed items. At crucial stages of the printing process, images of partially manufactured parts are captured and analyzed based on its geometry. Utilizing smartphones and the Internet of Things for efficient monitoring, the designed system aims to minimize material wastage, a prevalent concern in the creative food industry. The monitoring tool employs a 3D printer embedded with Raspberry Pi and a primary camera, with image processing carried out by the Obico plugin on Raspberry Pi to issue commands for real-time performance monitoring. The implementation of this technology not only addresses early defect detection but also enhancing food design and minimizing environmental impact, presenting promising prospects for the future of the food industry. © 2023 IEEE.

关键词： Internet of things

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The Application of Artificial intelligence in Geotechnical Investigation 3

The Application of Artificial Intelligence in Geotechnical I...

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3rd international conference on Artificial intelligence, Big Data and Algorithms, CAIBDA 2023

作者： Mei, Haifeng Zhang, Wei Gu, Jing Shaanxi Transportation Planning and Design Institute Co. Ltd China Xidian University School of Artificial Intelligence China

ISBN: (纸本)9781643684444

The standard penetration test (SPT) and dynamic probing test (DPT) are commonly used exploration methods in geotechnical investigation. However, errors can occur during data collection, often attributed to factors such as human error. To mitigate this issue, this paper proposes the utilization of an improved YOLOv5 object detection algorithm, a form of artificial intelligence technology, to automatically count the number of hammer strikes during geotechnical investigations. The proposed approach incorporates several enhancements to the YOLOv5 network architecture. Firstly, a focal loss function is introduced to address sample imbalance, ensuring better handling of different classes of hammer strikes. Additionally, online hard example mining technology is employed to improve model accuracy by focusing on challenging samples that are most informative for training. The improved YOLOv5 model is then applied to detect hammer strikes in SPT and DPT tests. To facilitate training and evaluation, a hammer detection dataset is created, tailored to the specific requirements of geotechnical investigation. Experimental results demonstrate the superior performance of the proposed improved YOLOv5 object detection model on the hammer detection dataset. © 2023 The Authors.

关键词： Object detection

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Image Processing-Based Real-Time detection and Tracking of Human 3

Image Processing-Based Real-Time Detection and Tracking of H...

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Ranjan, Saket Tyagi, Sarthak Gupta, Shubhi Kaur, Mandeep Kumar Goyal, Mayank School of Engineering & Technology Sharda University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350342338

The 'Image Processing-Based Real-Time detection and Tracking of Human' system were developed as a real-time approach to detect, identify and track people from a given video field or live stream. This can be used for security, surveillance, or missing person identification. Monitor people through CCTV and webcam video recordings. The system uses technologies such as OpenCV to process videos and images and produce expected results. The main goal of this system is to reduce human effort in manually identifying people in video streams. Using image processing techniques such as OpenCV, the system will be able to identify and track the person being trained. © 2023 IEEE.

关键词： Encoding Face Recognition HoG Open CV SVM

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Comparison of Performance of Machine Learning Algorithms for Diabetes detection 3

Comparison of Performance of Machine Learning Algorithms for...

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3rd international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Dalve, Pranav Bobby, Dyuti Marathe, Abha Dusane, Atharva Daga, Shreya Vishwakarma Institute of Technology Department of Electronics and Telecommunication Engineering Pune India

ISBN: (纸本)9781665494007

In today's society, Diabetes Mellitus affects a large portion of the population. The purpose of this research is to examine a few Machine Learning (ML) algorithms to assist in prediction of Type 2 diabetes, a disorder that alters the body's blood sugar processing. For the diabetes prediction proposed, the prominent PIMA Indian Diabetes Dataset was utilized. Support Vector Machine (SVM), K-Nearest Neighbours (KNN), XGBoost (Extreme gradient boosting), and Random Forest Regression are the techniques that were investigated. SVM yields an accuracy of 84.5%, sensitivity of 88.00% and specificity of 81.00%. KNN yields an accuracy of 80.5% sensitivity of 83.00% and specificity of 78.00%. XGBoost yields an accuracy of 83.41 %, sensitivity of 80.18% and specificity of 86.61%. Random Forest Regression yields an accuracy of 91.26%, sensitivity of 82.76% and specificity of 98.96%. Diabetes may be detected early and treated promptly, slowing the course of the disease. Using an ML model to reliably foresee Type 2 diabetes might prove beneficial. As a result, machine learning and artificial intelligence have found a home in the healthcare industry. The Random Forest method should be used for a far more accurate and specific identification, according to a comparison of the models. © 2023 IEEE.

关键词： Support vector machines

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Fast Point Cloud Registration for Urban Scenes via Pillar-Point Representation 3rd

Fast Point Cloud Registration for Urban Scenes via Pillar-Po...

引用

3rd CAAI international conference on Artificial intelligence (CICAI)

作者： Gu, Siyuan Huang, Ruqi Tsinghua Berkeley Shenzhen Inst Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9789819988495;9789819988501

Efficient and robust point cloud registration is an essential task for real-time applications in urban scenes. Most methods introduce keypoint sampling or detection to achieve real-time registration of large-scale point clouds. Recent advances in keypoint-free methods have succeeded in alleviating the bias and error introduced by keypoint detection via coarse-to-fine dense matching strategies. Nevertheless, the running time performance of such a strategy turns out to be far inferior to keypoint methods. This paper proposes a novel framework that adopts a pillar-point representation based feature extraction pipeline and a three-stage semi-dense keypoint matching scheme. The scheme includes global coarse matching, anchor generation and local dense matching for efficient correspondence matching. Experiments on large-scale outdoor datasets, including KITTI and NuScenes, demonstrate that the proposed feature representation and matching framework achieve real-time inference and high registration recall.

关键词： Point cloud registration Pillar-point representation Semi-dense keypoint matching

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Cyber Bullying and Toxicity detection Using Machine Learning 3

Cyber Bullying and Toxicity Detection Using Machine Learning

引用

3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Jadhav, Ranjana Agarwal, Naman Shevate, Srushti Sawakare, Chinmayee Parakh, Piyush Khandare, Snehankit Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9798350322842

The increased use of online platforms for communication has made cyberbullying and toxicity detection a critical issue in recent times. This paper explores the topic of cyberbullying and toxicity detection and proposes potential solutions for identifying cyber violence and offensive language more effectively. According to the study of the algorithms in examined research papers on cyberbullying and toxicity detection, this research study presents a novel approach that achieved 90% accuracy in identifying bully text in social media comments. This is done by using machine learning algorithms such as SVM, Logistic Regression, Naive Bayes, KNN, and Random Forest, with SVM and Random Forest exhibiting the best performance. Additionally, the system improved the accuracy of identifying bully images in social media posts to 84.5% by using the MobileNetV2 model (DNN), which is superior to other approaches. The system is trained using a large, labeled dataset of text data to identify and classify different types of cyberbullying and toxic content. The findings suggest that the proposed models hold promise in detecting instances of cyberbullying and offensive content effectively. These results have significant implications for the development of cyberbullying and toxicity detection systems. The proposed approach can be integrated into various social media platforms and online communities to identify and mitigate cyberbullying and toxic content more efficiently. The study also highlights the need for continued research and collaboration among stakeholders to address cyberbullying and toxicity effectively. © 2023 IEEE.

关键词： Social networking (online)

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Implementation of Cross-scene Target Tracking in Video Surveillance from the Perspective of Smart Campus 3

Implementation of Cross-scene Target Tracking in Video Surve...

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3rd international conference on Digital Society and Intelligent systems, DSInS 2023

作者： Wu, Fengjie He, Yan Liang, Zihui Liu, Jiarui Yang, Jisen Laboratory and Network Center Guangzhou University Guangdong Guangzhou510006 China School of Electronics and Communications Engineering Guangzhou University Guangdong Guangzhou510006 China Huamei Bond International College Guangzhou Huamei International School Guangdong Guangzhou510520 China

ISBN: (纸本)9798350331387

para>As the process of informationization in higher education institutions continues to deepen, campus construction is gradually progressing towards digitalization and intelligence. The video surveillance system, as an important part of smart campus construction, is also gradually transitioning from traditional manual monitoring to intelligent monitoring. This paper proposes a cross-scene target tracking in video surveillance method that integrated multiple deep learning algorithms to address the issue of target loss caused by occlusions or target movement across scenes. Specifically, the Siammask neural network is employed for target tracking in the video surveillance scenes. In cases where the tracked target is lost, the yolov3 neural network is utilized for individual detection in all surveillance scenes, and combines it with the strong baseline ReID and IBN-Net network to achieve the latest positioning of the target, thus enabling multi-angle and cross-scene target tracking. Finally, the effectiveness of this approach is validated through trial in the open-room video surveillance system of the experimental building in the campus. © 2023 IEEE.

关键词： Target tracking

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