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Classification of Inflammatory Gene Expression patterns with Machine Learning Models 4

Classification of Inflammatory Gene Expression Patterns with...

引用

4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Ay, Yeonglong Choo, Yit Hong Bhatti, Asim Lim, Chee Peng Stanford University Department of Neurology and Neurological Science Stanford United States Deakin University Institute for Intelligent Systems Research and Innovation Geelong Australia

ISBN: (纸本)9798350324303

Dementia is a complex neurological disorder characterized by a progressive decline in cognitive functions. Recent studies have suggested a link between neuroinflammation and the development of dementia, particularly in Alzheimer's disease. this study focuses on the analysis of inflammatory gene expression patterns in the parietal cortex (PCx) and temporal cortex (TCx) from a human brain RNA sequence data set, aiming to derive insights into the underlying genetic link associated with dementia, as well as prediction of dementia. Using five machine learning and statistical methods, our study reveals better detection and classification of dementia patients using PCx-related gene patterns, as compared with those from the TCx. Our findings support the hypothesis that the PCx plays a significant role in neuroinflammatory responses associated with dementia. Based on this preliminary study, machine learning models offer a useful approach to recognize inflammatory gene expression patterns and providing a valuable biomarker analysis and prediction for dementia diagnosis. © 2023 IEEE.

关键词： Gene expression

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Research on the Application of Image Preprocessing Technology Based on Computer Deep Learning in Wireless Communication systems 4

Research on the Application of Image Preprocessing Technolog...

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4th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2024

作者： Xu, Xiaoqing School of Information Engineering Eurasia University Shaanxi Xi'an China

ISBN: (纸本)9798350359589

this paper proposes a radio frequency signal identification method based on deep neural network. First, this article abstracts the radio frequency signal into a plane diagram and converts the radio frequency signal identification problem into object detection in image recognition. then, it is combined with the latest international artificial intelligence technology to further enhance the intelligence of radio frequency signals. At the same time, the ability to identify complex electromagnetic interference is enhanced. then this paper proposes a method to identify interference signals through radio spectrum images based on deep learning technology, and designs experiments to verify the identification accuracy of this method. the effectiveness of this method was verified through field sampling of a total of 5,432 samples of 15 categories. the proposed method can correctly identify the wave pattern and frequency characteristics of radio frequency signals, with an accuracy rate of 87.14%. the simulation results show that this method is universal and has good identification accuracy for interference at other frequency points. © 2024 IEEE.

关键词： convolutional neural network deep network image object detection radio frequency learning Radio wave recognition

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the Realization of a Collaborative Robot ACP System for intelligent Garbage Classification 4

The Realization of a Collaborative Robot ACP System for Inte...

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4th IEEE international conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Hu, Bin Zhang, Xiaodong Liu, Xinjie Xue, Rurui Cheng, Tao Qin, Minghua Li, Yun Xiong, Gang Dong, XiSong Qingdao Academy of Intelligent Industries China State Key Laboratory of Multimodal Artificial Intelligence Systems China Institute of Automation Chinese Academy of Sciences China Yantai Institute of Technology China

ISBN: (纸本)9798350349252

this paper addresses the global waste management crisis by proposing an innovative strategy that combines visual recognition technology with robotics, grounded in the ACP methodology, aiming to create an intelligent garbage classification system. the primary goal of the system is to handle the growing volume of waste by employing the YOLOv7 object detection model in conjunction with a Sawyer/Baxter collaborative robot to achieve effective garbage categorization. the study simulates the application process of ACP in a laboratory setting. Robots can select different 3D-printed soft grippers based on needs. Collaborative robots generate new data through actual operations, which is then used to update and improve the model, forming a continuous iterative closed-loop system. Despite environmental challenges, the system's achievements indicate potential for large-scale applications. Future research will focus on refining detection models, enhancing human-robot collaboration, and ensuring scalability for broader integration into waste management systems. © 2024 IEEE.

关键词： Collaborative robots

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Visibility Detection Methods in Road Traffic Scene-A Survey 4

Visibility Detection Methods in Road Traffic Scene-A Survey

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4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Jun, Zhou Tursun, Mamatjan Xinjiang University College of Intelligent Manufafturing and Mordern Industry Xinjiang University Urumqi China Xinjiang University College of Transportation Engineering Xinjiang University Urumqi China

ISBN: (纸本)9798350324303

Visibility is an important index to measure the clarity of visible objects in the atmosphere, which is of great significance to traffic safety, aviation navigation, environmental monitoring and other fields. Visibility detection is a process that uses sensors and algorithms to estimate and predict visibility levels. Different visibility detection methods were summarized, shortcomings and limitations of existing methods were summarized, principles and application of image based detection methods and neural network based algorithms were introduced. At the end, a conclusion concerning current methods and multimodal data fusion methods as future research interest were given to readers. © 2023 IEEE.

关键词： Visibility

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An intelligent recognition Method and System of 'Phubber' based on Object Detection and Pose Estimation Algorithm 4

An Intelligent Recognition Method and System of 'Phubber' ba...

引用

4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Liu, Tongshu High School Affiliated to Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350324303

the existing research works regard the recognition of 'phubber' as single task of object detection or human pose recognition. Its disadvantage is that its accuracy is insufficient, and it cannot effectively solve the problems of human body behavior understanding and human body overlaps. As a result, this paper provides an intelligent 'phubber' recognition method that integrates object detection and pose estimation algorithms. the basic idea is to use the human pose estimation network to predict key points of each human body in video frame image, and then comprehensively determine whether the human body is a 'phubber' according to the defined body behavior judgment rules, the type of hand-held object and distance of the hand-held objects from body. the experimental results show that this method outperforms existing baseline models of object detection in terms of recognition accuracy. As well, a system for cameras-based recognizing phubber' is developed based proposed method, and a verification of phubber' recognition on campus is conducted, reaching an overall recognition accuracy of 89.7%. © 2023 IEEE.

关键词： Object detection

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Proceedings - 4th IEEE 2023 international conference on Computing, Communication, and intelligent systems, ICCCIS 2023

Proceedings - 4th IEEE 2023 International Conference on Comp...

引用

4th IEEE international conference on Computing, Communication, and intelligent systems, ICCCIS 2023

ISBN: (纸本)9798350306118

the proceedings contain 194 papers. the topics discussed include: comparative study of DDoS detection and mitigation techniques;a machine learning-based blockchain model for the storage of maternal health records and safety prediction;enhancing digital investigation: leveraging ChatGPT for evidence identification and analysis in digital forensics;managing metadata in data warehouse for data quality and data stewardship in telecom industry – a compact survey;a review on detection and prevention of the DDoS attacks in the blockchain;analysis of face recognition technique: plastic surgery altered face;image classification using federated averaging algorithm;navigating the gray area: a three-label framework for uncovering uncertainty in fake news;and improvement in validation score with loss function for breast cancer detection using deep learning.

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Action recognition for intelligent Surveillance System using LRCN with attention mechanisms 4

Action Recognition for Intelligent Surveillance System using...

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4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Athilakshmi, R. Muddangula, Chandra Kiran Teja Tummala, Venkatesh Sainagakrishna, Pulavarthi Sri Chandan Kota, Sri Chaitanya Prasad, V. Jothi Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai600044 India Mnm Jain Engineering College Department of Information Technology Thoraipakkam Chennai97 India

ISBN: (纸本)9798350343670

Recognizing human actions is crucial across applications like video surveillance, human-computer interaction, sports analysis, and healthcare monitoring. Despite numerous models designed for image classification in the literature, there exists a research gap in enhancing video classification models. this study presents a new approach to improve action recognition in complex surveillance environments by employing attention-based Long Short-Term Memory Recurrent Convolutional Neural Networks (LRCN). Traditional methods face challenges in handling long video sequences, occlusions, and variations in lighting conditions. LRCN, combining Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) along with a self-attention layer, is proposed for its ability to capture both spatial and temporal information effectively. the hybrid architecture is designed to address issues such as handling long video frame sequences, occlusion, and variations in lighting, making it well-suited for real-world surveillance scenarios. the research aims to contribute to intelligent surveillance systems by improving accuracy and adaptability in recognizing actions in video frames. © 2024 IEEE.

关键词： Anomaly Detection CNN+RNN Convolutional Neural Networks intelligent Surveillance LRCN Real-time Monitoring Recurrent Neural Networks Security Suspicious Human Activity recognition

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Optimum signal duration for Human Activity recognition based on Deep Convolutional Neural Networks 18

Optimum signal duration for Human Activity Recognition based...

引用

18th Annual IEEE international systems conference (SysCon)

作者： Nazari, Farhad Shajari, Arian Nahavandi, Darius Deakin Univ Inst Intelligent Syst Res & Innovat IISRI Geelong Vic Australia

ISBN: (纸本)9798350358810;9798350358803

Human Activity recognition (HAR) stands as a pivotal technique within pattern recognition, dedicated to deciphering human movements and actions utilizing one or multiple sensory inputs. Its significance extends across diverse applications, encompassing monitoring, security protocols, and the development of human-in-the-loop technologies. However, prevailing studies in HAR often overlook the integration of human-centered devices, wherein distinct parameters and criteria hold varying degrees of importance compared to other applications. Notably, within this realm, curtailing the sensor observation period assumes paramount importance to safeguard the efficiency of exoskeletons and prostheses. this study embarks on the optimization of this observation period specifically tailored for HAR using Inertial Measurement Unit (IMU) sensors. Employing a Deep Convolutional Neural Network (DCNN), the aim is to identify activities based on segments of IMU signals spanning durations from 0.1 to 4 seconds. Intriguingly, the outcomes spotlight an optimal observation duration of 0.5 seconds, showcasing an impressive classification accuracy of 99.95%. this revelation holds immense significance, elucidating the criticality of precise temporal analysis within HAR, particularly concerning human-centric devices. Such findings not only enhance our understanding of the optimal observation period but also lay the groundwork for refining the performance and efficacy of devices crucially relied upon for aiding human mobility and functionality.

关键词： Human Activity recognition Human-in-the-loop Classification Convolutional Neural Network

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Logistics 4.0 - Monitoring of Transport Trolley in the Factory through Vision systems Using the YOLO Model Based on Convolutional Neural Networks 4th

Logistics 4.0 - Monitoring of Transport Trolley in the Facto...

引用

4th international conference on intelligent systems in Production Engineering and Maintenance, ISPEM 2023

作者： Poskart, Bartosz Iskierka, Grzegorz Krot, Kamil Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783031442810

the aim of the paper is to present the concept of integrating an image recognition system based on elements of artificial intelligence with the in-house monitoring system, thus creating an intelligent system for supervising intralogistics transport trolleys. the work also presents a complete methodology for preparing a system using the YOLOv5 model for image analysis and classification. the work also presents the profits from the implementation of new algorithms and new methods of using already existing systems in the company. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： intelligent systems

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Research on Machine Vision intelligent Detection System Based on Faster R-CNN 4

Research on Machine Vision Intelligent Detection System Base...

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4th IEEE international conference on Data Science and Computer Application, ICDSCA 2024

作者： Wang, Xueting Zeng, Fanshuo Song, Weizhao Zhang, Zhipeng He, Huitong Zhou, Zheyuan Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9798350368239

With the rapid development of artificial intelligence technology, visual inspection and image processing algorithms have been continuously improved in accuracy and efficiency, and intelligent inspection systems based on machine vision have been widely used in product quality control, defect detection and process monitoring. In this paper, a machine vision intelligent inspection system based on Faster Region-based Convolutional Network (Faster R-CNN) is proposed, which aims to improve the detection accuracy and efficiency. In this paper, the overall architecture of the system is introduced, including the image acquisition module, the data preprocessing module and the detection module based on Faster R-CNN. In the data preprocessing module, the Deep Convolution Generative Adversarial Networks (DCGAN) algorithm was proposed for image expansion generation, and high-resolution image samples that were basically consistent with the collected image datasets were obtained. then, the Faster R-CNN model was optimized and adjusted for specific application scenarios, so that the average accuracy (mAP) of the model reached 78.23% and the FPS value reached 11.0. ©2024 IEEE.

关键词： Faster R-CNN Image Processing image recognition Machine vision intelligent inspection system

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