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Research on Object detection Algorithm in Complex Scene Based on Improved YOLOv5 3

Research on Object Detection Algorithm in Complex Scene Base...

引用

3rd international conference on Artificial intelligence, Virtual Reality, and Visualization, AIVRV 2023

作者： Zhou, Wanzhen Chen, Yi Dun, Sicheng Wu, Huicong Department of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China FedUni Information Engineering Institute Hebei University of Science and Technology Shijiazhuang050000 China

ISBN: (纸本)9781510671485

The main task of object detection is the recognition and localisation of objects in images. It is the cornerstone of image understanding and the preparation for vision tasks such as image segmentation, target tracking and pose recognition. YOLOv5 is a object detection technique that is widely used in military, medical and traffic applications. However, in complex and changing real-world scenes, there are often multiple interfering factors, so the detection effect of YOLOv5 is greatly affected. In this paper, we propose an improved YOLOv5 algorithm for complex scenes from the perspectives of background information interference and inaccurate bounding box localisation. Firstly, the SimAM attention module is added to YOLOv5 to enable the model to focus on the differences in information in the feature maps and to enhance the anti-interference ability of the network;then the EIoU loss is used in the loss function and the Focal EIoU loss function is proposed by combining the optimization idea of Focal loss to deal with the category imbalance and improve the convergence speed of the algorithm;finally, a comparison experiment is conducted on the PASCAL VOC dataset to demonstrate the reliability of the improved algorithm. © 2023 SPIE.

关键词： Object detection

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Multimodal Mental Workload Classification Using Maus Dataset 3

Multimodal Mental Workload Classification Using Maus Dataset

引用

3rd international conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Sethi, Amanrosekaur Walambe, Rahee Jain, Priyanka Kotecha, Ketan Symbiosis Institute of Technology Symbiosis International University Pune India CDAC New Delhi New Delhi India

ISBN: (纸本)9798350348347

This study aims to use machine learning to detect stress in individuals to enhance their quality of life, through stress management techniques. We propose supervised learning-based classifiers model for the detection of stress in humans using a MAUS dataset which is multimodal and is publicly accessible. MAUS focuses on the investigation of wearable device-based mental workload (MW) assessment system. The database contains records of fingertip PPG, galvanic skin reaction (GSR), electrocardiography (ECG) and wrist-worn PPG. For multimodal fusion, the normalized features of ECG and PPG (fingertip and wrist) are concatenated, and early fusion based classifier methods are applied. The accuracy and F1 score for combined modalities namely, ECG, PPG (fingertip) and PPG (wrist) is reported at 75.7% and 73.9% using AdaBoost Classifier. © 2023 IEEE.

关键词： Electrocardiography

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Realistic Condition-Based Anomaly detection of Multi-Faults in Rotating Machines 3

Realistic Condition-Based Anomaly Detection of Multi-Faults ...

引用

3rd IEEE international conference on Sustainable Energy and Future Electric Transportation, SeFet 2023

作者： Mishra, Rismaya Kumar Choudhary, Anurag Fatima, Shahab Mohanty, Amiya Ranjan Panigrahi, Bijiaya Ketan Indian Institute of Technology Delhi Centre for Automotive Research and Tribology New Delhi India School of Interdisciplinary Research Indian Institute of Technology Delhi New Delhi India Indian Institute of Technology Department of Mechanical Engineering West Bengal Kharagpur India Indian Institute of Technology Delhi Department of Electrical Engineering New Delhi India

ISBN: (纸本)9798350319972

Faulty machine components often get replaced instead of repaired in industries. The involved cost and downtime increase due to this problem, which can be mitigated with proper maintenance planning. This paper proposes a multi-fault diagnosis of two critical rotating components, motor and bearing at a system level. Motor current signatures of all possible muti-fault conditions were acquired at different speeds. The extracted signals were divided into small signal segments by implementing a sliding window with a fixed sample size. Continuous Wavelet Transform (CWT) was then employed on each extracted segment, and different datasets were formed for doing signal, sequence and image classification using Artificial Neural Network (ANN), 1D Convolutional Neural Network (CNN) and 2D CNN models, respectively. A comparative analysis of model performance was performed and found that 2D CNN outperformed other machine learning models. The Motor Current Signature Analysis (MCSA) could be used for system-level multi-fault anomaly detection using machine learning algorithms. © 2023 IEEE.

关键词： Fault detection

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Enhanced Pneumonia detection Through Cough Analysis Techniques 3

Enhanced Pneumonia Detection Through Cough Analysis Techniqu...

引用

3rd international conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Ramesh Babu, A. Sathvika, V. Junaid, Abdul Mannan Abhi Ram, S. Reddy, V.Akhila Shiva Shankar Reddy, K. SR University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350343632

Millions of individuals worldwide are afflicted with pneumonia, a potentially fatal respiratory infection that is an inflammatory disease that affects the air sacs in the lungs. For prompt intervention and successful treatment, pneumonia must be identified early. The proposed approach analyzes cough sounds and recognizes patterns linked to pneumonia by using 1D-CNN and sophisticated signal processing techniques. One promising non-invasive, low-cost method for detecting pneumonia may be cough sound analysis. A plethora of information about the respiratory system can be found in cough sounds, and recent advances in machine learning have made it possible to create accurate and trustworthy frameworks for the detection of pneumonia from cough sounds. Our results propose that cough sound analysis has the potential to be a valuable apparatus for pneumonia detection in resource-limited settings. Our framework is simple to utilize and does not require any specialized equipment, making it perfect for utilize in community health clinics and other inaccessible areas. The algorithm showed 91.35% test accuracy for the 82 cases of cough sound in pneumonia group and 91 cases in non-pneumonia group. © 2023 IEEE.

关键词： Audio Processing Cough Analysis Deep learning Feature Extraction Machine learning Pneumonia detection

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An intrusion detection model using smote and ensemble learning 3

An intrusion detection model using smote and ensemble learni...

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3rd international Seminar on Artificial intelligence, Networking, and Information technology, AINIT 2022

作者： Qiu, Ling-Feng Song, Ya-Fei College of Air and Missile Defense Air Force Engineering University Xi'an710051 China

ISBN: (数字)9781510662971

ISBN: (纸本)9781510662964

As a security defense technology to protect the network from attack, network intrusion detection system plays a very important role in the field of computer system and network security. Aiming at the multi classification problem of unbalanced data in network intrusion detection, machine learning has been widely used in intrusion detection, which is more intelligent and accurate than traditional methods. The existing multi classification methods of network intrusion detection are improved, and an intrusion detection model using smote and ensemble learning is proposed. The model is mainly divided into two parts: smote oversampling and stacking classifier. The NSL-KDD dataset is used to test the Stacked Ensemble model in this paper. Compared with the other five basic learner models, the Stacked Ensemble has a higher detection rate. Stacked Ensemble has significant advantages in solving the multi classification problem of unbalanced network intrusion detection data. It is a practical and feasible intrusion detection method. © 2023 SPIE.

关键词： Learning systems

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Research on Online Teaching Emotion detection based on Federated Learning 23

Research on Online Teaching Emotion Detection based on Feder...

引用

3rd international conference on Bioinformatics and Intelligent Computing, BIC 2023

作者： Chen, Caiyun Dept. of Intelligent Science and Technology Xiamen University Tan Kah Kee College Xiamen China

ISBN: (纸本)9798400700200

With the integration of artificial intelligence and education, online education has received unprecedented attention. The new infrastructure system is improved, and artificial intelligence is widely used in teaching, management, resource construction and other fields. At present, the research on intelligent education is still focused on improving students' learning efficiency, intelligent education monitoring and other aspects, but there is no relevant research on the evaluation system of AI-assisted teachers' teaching level and effect. In fact, students' learning effect is not only related to their own efforts, advanced educational equipment or not, but also closely related to the education level and style of teachers. We propose a Federated-learning based online teaching emotion recognition detection for teachers. Through the recognition of teachers' teaching emotion, it can realize the auxiliary analysis of the teaching level of teachers, help to find the problems in the process of online teaching and improve the teaching level of teachers. At the same time, on the basis of protecting the privacy security and legal compliance of teachers' teaching data, the Federated learning realization realizes data sharing, joint modeling, and improves the recognition effect. The experimental results show that the modified model can realize the collaborative training of participants under the independent and unbalanced data, and realize the effective recognition of facial emotion in online education teaching of teachers. © 2023 ACM.

关键词： Emotion Recognition

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Diabetes Prediction Using Nature-Inspired Optimization Algorithms 3

Diabetes Prediction Using Nature-Inspired Optimization Algor...

引用

3rd international conference on Artificial intelligence and Signal Processing, AISP 2023

作者： Vijaya, J. Tirkey, Divyanshu Singh, Priyank Goel, Aditya International Institute of Information Technology Department of DSAI Chhattisgarh Naya Raipur India

ISBN: (纸本)9798350320749

Diabetes is a common genetic disease across the world. It is a chronic disease spurred by either production of insufficient production of insulin by the pancreas or less insulin use by the body. Insulin maintains the blood sugar level of the body. Uncontrolled diabetes can cause hypertension, also known as high blood glucose. Hypertension in the long run can be a cause of heart disease. Therefore, the prediction of diabetes is a major problem in the health sector. Various machine learning methods are already in use for the prediction of diabetes. Traditional methods were first used to address diabetes forecasting, but they take a while to deploy. Therefore, future research has adopted the use of various optimization strategies to solve the issue of diabetes forecasting as well as to boost system selectivity and operating speed. These methods have all proven to be successful in solving different optimization issues and also improve the model significantly. This work proposes a Logistic Regression model by the use of nature-inspired algorithms. We proposed a methodology which uses seven different optimization algorithms instead of gradient descent for linear regression. We implemented a linear regression algorithm from scratch and then used it with different optimization algorithms which improve the overall performance. © 2023 IEEE.

关键词： Blood

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detection of Insider Threats Using Deep Learning 3

Detection of Insider Threats Using Deep Learning

引用

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

作者： Anju, A. Nithya Kalyani, M. Shalini, K. Ravikumar, Haritha Saranya, P. Krishnamurthy, M. Kcg College of Technology Department of Information Technology Tamilnadu Chennai India Kcg College of Technology Department of CSE Tamilnadu Chennai India

ISBN: (纸本)9798350322842

Information technology systems are more and more susceptible to many variety of security risks, the majority of risks are mostly started by internal users. Insider threats are a complicated and difficult problem to identify and avoid because they mostly have privileged access to the network and database of company details. Businesses, organisations, and governmental organisations have a serious cybersecurity risk as a result of insider attacks. Insider threat identification is challenging because of uneven data, scant ground truth, and potentially changing user behavior. This work provides an insider threat detection method based on anomaly detection using unsupervised learning. Computer network and system security are severely hampered by insider threats. Theft of intellectual property, sabotage, the release of sensitive data, and web application attacks are examples of malevolent actions committed by authorised users that have the potential to do serious harm. It is the responsibility of the organisation to protect all network layers and guard against intrusions. Using historical data, the extracted behavioral traits using the Deep Learning method. The project made advantage of the publicly accessible Computer Emergency Response Team (CERT) insider threats dataset. © 2023 IEEE.

关键词： Unsupervised learning

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Fault detection Methodology for PV Array 3

Fault Detection Methodology for PV Array

引用

3rd international conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2023

作者： Shekhar, Anshul Senthil Kumar, M. National Institute of Technology Department of Electrical Engineering Patna India

ISBN: (纸本)9798350307399

The photovoltaic (PV) array is one of the most important renewable energy resources, as the power generation from PV contributes significantly to the system load among renewable power generation. Further, the energy generation is free of pollution and cost, as it generates power from abundant sunlight using the photovoltaic effect. Though there are various factors, such as variations in sunlight irradiance, sunlight hours, atmosphere temperature, etc., that affect the energy generation in PV, the fault in the PV array is one of the crucial factors that affects it severely. Each fault in the PV array has its own distinct characteristics. These characteristics may be detected using the various methods proposed. This paper presents a comprehensive analysis of protection methods implemented for detecting PV array faults. It discusses the features and limitations of each protection methodology elaborately. It also describes the causes of each fault and its characteristics in detail. © 2023 IEEE.

关键词： Photovoltaic effects

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Malware detection Method Based on TPE Optimized Stacking 3

Malware Detection Method Based on TPE Optimized Stacking

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3rd international conference on High Performance Computing and Communication Engineering, HPCCE 2023

作者： Zhao, Dongbo Yuan, Zesong Xie, Nannan Jilin Province Key Laboratory of Network and Information Security School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

Fast detection and categorization of malware are increasingly important for securing hosts and networks. Although many Machine Learning models have been utilized to detect malware, single-model detection may not remain efficient in the face of diverse datasets. The continuously increasing size of malware need the distributed storage systems and distributed computing. The distributed methods and ensemble learning are introduced into malware detection, and an optimal combination of base classifiers suitable for stacking are designed. A hyperparameter optimization method based on the Tree-structured Parzen Estimator (TPE) approach to enhance malware detection. The proposed method is implemented on Apache Spark and Hadoop Distributed File system(HDFS). Experiments conducted on four independent datasets, including Android and Windows, demonstrate that the proposed method can achieve 99.41% accuracy on the Android dataset and 96.96% accuracy on the Windows dataset. © 2024 SPIE.

关键词： Learning systems

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