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computer vision Based Vulnerable Road Users Hand Signal Recognition 3

Computer Vision Based Vulnerable Road Users Hand Signal Reco...

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3rd international conference on Inventive Research in Computing Applications, ICIRCA 2021

作者： Ashtekar, Sunidhi Dhalwar, Suraj Pasupathy, Anuradha Continental Automotive India Bengaluru India

ISBN: (纸本)9780738146270

With development of computer vision (CV) and Artificial Intelligent (AI) the domains like object detection, object tracking their development from 2D to 3D tracking and human pose estimation have gained a lot of popularity and demand in the field of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD). Basically, these systems are designed with an objective to increase the safety and avoid road accidents while driving. These systems develop the ability of car driver to react to various types of hazards and dangers on the road. If it is possible to know the intentions of Vulnerable Road Users (VRUs) like pedestrian, cyclist or motorbike riders then the probability of road accidents can be reduced to great extents. The ADAS systems are able to warn the car driver through automatic systems and thus provide high safety. This paper proposes Convolutional neural Networks (CNN) based VRUs hand signal recognition system based on Region Multi-Person Pose Estimation (RMPE) [1] framework for human pose estimation to know the hand signals given by the bicyclist, scooter, e-scooter and motorbike riders to warn the car driver. The VRUs are well aware of the traffic rules and regulations and also follow them. Based on the signal given by bicyclist the ego-vehicle can stop or reduce its speed. The focus of this novel algorithm proposed in this paper is that it is capable of recognizing all the six hand signals of bicyclists left, right, stop, give way, slow down and road hazard which are not addressed before in any previous works. Also, this algorithm can recognize arm signals of scooter, e-scooter riders and motorbike riders efficiently. The dataset used for testing is the self-captured dataset for VRUs Arm Signal Recognition and to generalize it Cyclist Arm Signal Recognition (CASR) [2] dataset is also used. The proposed work provides state-of-the-art (SOTA) results for VRUs hand signal recognition and frame rate of 10 FPS and 92% acuuracy. © 2021 IEEE.

关键词： Convolutional neural networks

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Comparative Study between Leading Transfer Learning Architectures for Source Camera Identification 2

Comparative Study between Leading Transfer Learning Architec...

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2nd international conference on Power, control and Computing Technologies, ICPC2T 2022

作者： Chakravarty, Shreya Fating, Shardul Jain, Ishita Varun, Ishika Khandelwal, Richa R. Shri Ramdeobaba College of Engineering and Management Department of Electronics Engineering Nagpur India

ISBN: (纸本)9781665458580

The all-embracing use of digital images has revamped the quality of life and security to a great extent. Right from finding an item on online shopping websites through a clicked picture, to CCTV cameras being used for road traffic control, the users have learnt to appreciate the existence of technology being as advanced. However, one cannot overlook the gravity of this technology being misused. Although, the digitization has incorporated advanced concepts like computer vision and Deep Learning for security-check and crowd control, this has encouraged the advancement of courtroom discussions. Framing people for wrongdoings they are not involved with, on the basis of a fake 'digital proof,' is one of the newly faced muddles. False allegations on a person, on the basis of a picture or a video, can potentially put a question on the existence of a person. The need to find the legitimacy of a produced image is therefore, of utmost importance. There have been various studies over the years, wherein a lot of methods were proposed to develop a system that identifies the camera model. Through this paper, we aim to produce a comparative study between four leading architectures, DenseNet, Inception V3, MobileNetV2 and Exception(XCeption), and suggest a the most competent architecture for commercialization of this system. © 2022 IEEE.

关键词： Deep learning Digital images Transfer learning Power control computer architecture Cameras Security

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Research on Water Leakage Fault Detection of Air Conditioning and Refrigeration Plant Room Based on computer vision Technology

Research on Water Leakage Fault Detection of Air Conditionin...

引用

computer Science, Electronic Information Engineering and Intelligent control Technology (CEI), IEEE international conference on

作者： Rundong Liu Zhengwei Li Rui Zhou Yuxuan Ding Shiyang Wen Xiujin Zhu College of Mechanical Engineering Tongji University Shanghai China Suzhou University of Science and Technology Suzhou China

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

Air conditioning and refrigeration plant rooms have always been at risk of equipment damage and energy wastage due to water leakage faults. At present, only manual methods of water leakage fault detection, especially in the dark light environment, are obviously unable to avoid the potential risks. In this paper, we propose an improved algorithm YOLO-SEE (SENet+EIOU) based on computer vision technology to identify water leakage faults in potential leakage areas of air conditioning and refrigeration plant rooms under different lighting environments to reduce the risk of subsequent damage caused by faults. The self-made water leakage dataset of refrigeration plant rooms has 2485 images, the attention mechanism SENet is inserted into the backbone part of the model to improve the feature extraction ability of the model, the loss function EIOU is improved to increase the convergence speed of the model and provide better location results, and the performance of the algorithm is validated by comparing the algorithms such as SSD, YOLOv5s and etc. on the test set. The experimental results show that the improved YOLO-SEE algorithm achieves an average precision of 88.9% for water leakage detection in air-conditioning and refrigeration plant rooms, and still ensures the accuracy and real-time performance under different lighting environments, which can meet the needs of real-time monitoring of water leakage faults in actual refrigeration plant rooms.

关键词： Fault diagnosis Air conditioning YOLO computer vision Atmospheric modeling Fault detection Lighting

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Artificial Intelligence, Intelligent Manufacturing, and Sustainable Futures in Next-Gen Textiles: A Comprehensive Approach

Artificial Intelligence, Intelligent Manufacturing, and Sust...

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Sathika Begum Mohammad Shabi Bhuvaneshwari M S. Kanimozhi S Pavithra Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The next revolutionary innovation in the textile industry is artificially driven by intelligent quality control and next-generation textile materials. This review, based on AI-driven automation and sustainable practices in textiles, provides a comprehensive overview of how emerging technologies are reshaping manufacturing processes, quality control, and functionalities in textiles. It examines the use of machine learning in defect detection, the integration of smart materials, and the adoption of recycled and bioengineered fabrics, thus helping producers to overcome issues associated with fabric production, environmental impact, quality control, and scalability. The synthesis of recent advances enables this study to critically review literature and case studies on AI, smart materials, and sustainable practices in textiles. It lays a pathway for future developments and advancements in the textile industry.

关键词： Reviews Quality control Production Fabrics Manufacturing Quality assessment Artificial intelligence Sustainable development Textile industry Defect detection

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Simulations and Advancements in MRI-Guided Power-Driven Ferric Tools for Wireless Therapeutic Interventions

Simulations and Advancements in MRI-Guided Power-Driven Ferr...

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AI in Cybersecurity (ICAIC), international conference on

作者： Wenhui Chu Aobo Jin Hardik A. Gohel Dept. of Computer Science University of Houston-Victoria Victoria USA

Designing a robotic system that functions effectively within the specific environment of a Magnetic Resonance Imaging (MRI) scanner requires solving numerous technical issues, such as maintaining the robot’s precision and stability under strong magnetic fields. This research focuses on enhancing MRI’s role in medical imaging, especially in its application to guide intravascular interventions using robot-assisted devices. A newly developed computational system is introduced, designed for seamless integration with the MRI scanner, including a computational unit and user interface. This system processes MR images to delineate the vascular network, establishing virtual paths and boundaries within vessels to prevent procedural damage. Key findings reveal the system’s capability to create tailored magnetic field gradient patterns for device control, considering the vessel’s geometry and safety norms, and adapting to different blood flow characteristics for finer navigation. Additionally, the system’s modeling aspect assesses the safety and feasibility of navigating pre-set vascular paths. Conclusively, this system, based on the Qt framework and C/C++, with specialized software modules, represents a major step forward in merging imaging technology with robotic aid, significantly enhancing precision and safety in intravascular procedures.

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A Comprehensive Review on Table Driven Routing Strategies in MANET 3

A Comprehensive Review on Table Driven Routing Strategies in...

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3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Sharma, Shelja Srivastava, Ritesh Sharma, Vishnu Tayal, Veena Galgotias College of Engineering Technology Department of Computer Science Engineering India Galgotias College of Engineering Technology Department of Information Technology India

ISBN: (纸本)9781665438117

Dynamic topology imposes challenges in designing an efficient routing protocol, for enabling successful communication between mobile devices in MANET. Based upon nature of working, routing protocols are broadly classified into proactive and reactive protocols. This work is focused on the review of work done by many researchers in the direction of improving the performance of table-driven routing strategies viz., Destination Sequenced Distance Vector (DSDV) and Optimized Link State Routing (OLSR) protocol. Specifically, the efforts made by various researchers to improve the conventional MPR selection strategy of OLSR have been explored, to identify the context for new research. Furthermore, review on energy considerations of mobile nodes in OLSR to improve its performance, has helped in understanding the future scope. This paper provides insights to researchers working in the area to further explore the new research directions. © 2021 IEEE.

关键词： Routing protocols

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Research Progress of Fine-grained Visual Classification: Basic Framework, Challenges, and Future Development 3

Research Progress of Fine-grained Visual Classification: Bas...

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3rd international Academic Exchange conference on Science and Technology Innovation, IAECST 2021

作者： Ma, Congying Pu, Yuhan University of Bath Department of Computer Science Somerset Bath United Kingdom University of California Department of Statistics DavisCA United States

ISBN: (纸本)9781665402675

With the progress of multimedia technology, fine-grained visual classification (FGVC) has gradually attracted extensive attention in the field of computer vision. Thanks to the success of deep neural networks, both the accuracy and speed have achieved an unprecedented breakthrough. Hundreds of fine-grained classification techniques have been developed from the early, fully supervised methods to the current weakly ones. To this end, in this article, we provide a comprehensive survey of the recent achievements of FGVC. Specifically, we describe the formulation and setting of the FGVC, including the background, basic framework, and challenges. Then, we introduce the widely used datasets and compare the performance of different models. Lastly, we discuss the potential future directions of FGVC. © 2021 IEEE.

关键词： Deep neural networks

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CNN Implementation for IDS 3

CNN Implementation for IDS

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3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Varanasi, Venkata Ramani Razia, Shaik Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9781665438117

Real world data is more complex in nature and requires proper handling to use with machine learning techniques. Handling large volumes of data is the key aspect in deep learning (DL). As new attacks are evolving, traditional Intrusion detection systems (IDS) are unable to handle those complex attacks. With successful applications of convolutional neural networks (CNN) in other fields like object detection, face recognition, healthcare, this paper proposes CNN based IDS using CIC-IDS-2017 dataset. The performance of the model is compared with that of a deep neural network (DNN). The numerical features are converted into gray scale images and fed to the model. The model is repeated with different values of hyper parameters and results are presented. CNN model can be considered as useful for transfer learning as it reduces training time and gives better accuracy by considering the correlation between the features. The authors implemented CNN model with the objective of applying it to homogeneous and heterogeneous transfer learning in the future. CNN model attained an accuracy of 99% as compared to DNN with 98%. © 2021 IEEE.

关键词： Deep neural networks

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Fake News Detection using Hybrid of Deep Neural Network and Stacked LSTM 3

Fake News Detection using Hybrid of Deep Neural Network and ...

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3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Narayan, Udit Kumar, Ajad Kumar, Kuldeep Dr. B R Ambedkar National Institute of Technology Jalandhar Department of Computer Science and Engineering Punjab Jalandhar India

ISBN: (纸本)9781665438117

Fake News is one of the major concerns for the world and it could cause many problems like misleading people, the potential to influence people's opinions, etc. Hence distinguishing fake news is very crucial. Fake news can be detected using artificial intelligence methodologies using a title, body of the news, and other attributes such as credibility of the authors, location, media such as images, audio, and video, etc. In this paper, a hybrid of deep neural networks and stacked LSTM (Long Short-Term Memory) is used to detect fake news. We used Glove 300d as a word embedding layer and implemented the concept of stacked LSTM. The model has been applied to two different datasets. The results are compared with previous models and have found with good performance in comparison to the selected previously existing models. © 2021 IEEE.

关键词： Deep neural networks

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House Price Prediction Using Machine Learning 3

House Price Prediction Using Machine Learning

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3rd international conference on Advances in Computing, Communication control and Networking, ICAC3N 2021

作者： Singh, Akhilendra Pratap Rastogi, Kartikey Rajpoot, Shashank Galgotias University Computer Science Greater Noida India

ISBN: (纸本)9781665438117

Now-a-days everyone wish to live in the large cities but the competition in the market related to all the resources is increasing day by day. A middle-class family can't afford the price of rent, food, water and electricity while surviving his family. The price of the flats in the city is increasing and there is so much of risk to predict the actual price of the house. Our research paper [1] will helps you to predict the price of the house to a good accuracy. The main motive of our research paper is to predict the price [2] of the house by analyzing the customer needs and their financial income. As we see when a client wants to purchase the house in the city he used to see three things within the city location, area and available resources around the society. Our research paper will helps the clients to know the actual price of the house and it will also helps the builders to know about the selling price that will fits the client needs. © 2021 IEEE.

关键词： Decision trees

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