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Utilizing Decision Tree,SVM And KNN Algorithms For Optimal Speed Detection And control With Superior Accuracy

Utilizing Decision Tree,SVM And KNN Algorithms For Optimal S...

引用

2024 international conference on communication, computing and Internet of Things, IC3IoT 2024

作者： Ramya, N. Amrutha, K. Krishalini, H. Ramya, K. Sri Sairam Engineering College Department of CSE Chennai India

ISBN: (纸本)9798350352689

Over-speeding is a major contributor to traffic accidents and fatalities worldwide. AI and ML algorithms like decision tree, SVM and KNN can be used within vehicles to automatically detect and control speed limits, thus improving road safety. This paper explores the potential of AI/ML-based systems for vehicle speed limit detection and control, analyzing their functionalities, challenges and future directions. The paper finds that AI/ML-based systems can effectively detect and control speed limits, leading to a significant reduction in traffic accidents and fatalities. Improving algorithm accuracy and ensuring data reliability are fundamental to unlock the full potential of this technology. The paper also discusses some future directions for AI/ML-based speed limit detection and control, such as the use of more sophisticated algorithms and the development of more comprehensive data sets. © 2024 IEEE.

关键词： Decision trees

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Dental Diagnostic Imaging in AI-based System: A Systematic Mapping Study

Dental Diagnostic Imaging in AI-based System: A Systematic M...

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2024 international conference on control and automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Supriyadi, Muhamad Rodhi Samah, Azurah Bte A Muliadi, Jemie Awang, Raja Azman Raja Shah, Zuraini Ali Majid, Hairudin Abdul Ismail, Noor Huda Universiti Teknologi Malaysia National Research and Innovation Agency Bandung Indonesia Universiti Teknologi Malaysia Faculty of Computing Johor Malaysia Research Center AI and Cyber Security National Research and Innovation Agency Bandung Indonesia School of Dental Sciences Universiti Sains Malaysia Kelantan Malaysia

ISBN: (纸本)9798331511166

The analysis of human dental images is a complex and time-consuming task for a dentist, owing to the irregular and inconsistent various teeth's stuctures. Such analysis is required to detect tooth-related issues, anomalies, or alterations in tooth structure. Therefore, automation is crucial for dental image segmentation and inspection to guarantee accurate diagnosis and improved treatment planning. This article presents a thorough examination and analysis of dental image segmentation. It explores around 62 research works done between 2019 and 2023, utilizing several dental imaging modalities. The survey provides comprehensive information on the latest techniques, encompassing several imaging modalities, performance metrics, datasets employed, and the study's objectives. © 2024 IEEE.

关键词： Mapping

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Bidirectional communication control for Human-Robot Collaboration 39

Bidirectional Communication Control for Human-Robot Collabor...

引用

IEEE international conference on Robotics and automation (ICRA)

作者： Ferrari, Davide Benzi, Federico Secchi, Cristian Univ Modena & Reggio Emilia Dept Sci & Methods Engn Modena Italy

ISBN: (纸本)9781728196817

A fruitful collaboration is based on the mutual knowledge of each other skills and on the possibility of communicating their own limits and proposing alternatives to adapt the execution of a task to the capabilities of the collaborators. This paper aims at reproducing such a scenario in a human-robot collaboration setting by proposing a novel communication control architecture. Exploiting control barrier functions, the robot is made aware of its (dynamic) skills and limits and, thanks to a local predictor, it is able to assess if it is possible to execute a requested task and, if not, to propose alternative by relaxing some constraints. The controller is interfaced with a communication infrastructure that enables human and robot to set up a bidirectional communication about the task to execute and the human to take an informed decision on the behavior of the robot. A comparative experimental validation is proposed.

关键词： automation Collaboration Bidirectional control Behavioral sciences Task analysis Robots

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Embedded System Based Intelligent Anti-Troxler Headlamp 5

Embedded System Based Intelligent Anti-Troxler Headlamp

引用

5th international conference on control, communication and computing, ICCC 2023

作者： Nithin, S.S. Viji, R. Sreelatha, G. Santhoshkumar, S. APJ Abdul Kalam Technological University Electronics and Communication Engineering College of Engineering Trivandrum Trivandrum India APJ Abdul Kalam Technological University Electronics and Communication Engineering Government Engineering College Idukki Idukki India

ISBN: (纸本)9798350334128

The conventional headlamp consists of a body of lights and simple optics to direct the light onto the road. Using high beams at night may cause discomfort to the other drivers on the road. This may lead to accidents. The Intelligent Anti Troxler Headlamps modulate the light throw of the projector headlamps with the help of vision sensors, spatial light modulators and object detection. This can be made possible with the advancements in machine learning as well as the advancements in the field of spatial light modulations. Object detection and image processing will be carried out on the Nvidia Jetson Nano kit. This increases the visibility of the driver and eliminates the glare which causes discomfort to the other drivers on the road due to the headlamps. © 2023 IEEE.

关键词： Object detection

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Transmission delay control technology of low voltage carrier reading system based on micro power wireless communication 2

Transmission delay control technology of low voltage carrier...

引用

2nd international conference on Informatics, Networking, and computing, ICINC 2023

作者： Li, Ze Liu, Xiaoze Shi, Mingce Bao, Xinzhu Bi, Yue State Grid Liaoning Marketing Service Center Shenyang China

ISBN: (纸本)9781510674745

Low-voltage carrier reading system uses power line as transmission medium, and the power line has high loss and attenuation characteristics, so the signal is easily disturbed by noise during transmission, resulting in greater difficulty in transmission delay control. Therefore, a transmission delay control technology for low-voltage carrier aggregation system based on micro-power wireless communication is proposed. Support vector machine is used to extract micro power wireless communication features. Based on this, a delay predictor is designed to predict the delay of low-voltage carrier reading system by using the three-input, single-input and single-layer linear neuron structure of neural network. By using Hidden Markov model (HMMs), the current delay is estimated based on the transmission delay data of the previous time, and the transmission delay control of low-voltage carrier reading system is realized. The experimental results show that the throughput and fitting rate of the research method are high, and the delay can be significantly reduced under the application of the research method. © 2024 SPIE.

关键词： Support vector machines

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Deep Learning based Automated Parking Lot Space Detection using Aerial Imagery 2

Deep Learning based Automated Parking Lot Space Detection us...

引用

2nd international conference on Advancements in Electrical, Electronics, communication, computing and automation, ICAECA 2023

作者： Gopinath, B. Gokul, K.S. Pumenitha, S.T. Vasanth, S.V. Hema Kumaraguru College of Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350306811

The development of an automated car parking lot space detection system with deep learning technique and OpenCV is presented in this paper. The proposed system utilizes a convolutional neural network (CNN) to detect and classify cars in parking spaces. The system takes the features and advantages of CNNs to learn and recognize complex patterns in aerial images. To train the CNN, a dataset of parking lot images with labelled parking spaces is created. The training dataset is then used to optimize the CNN's ability to detect and classify cars in parking spaces. The outcome of the developed system is evaluated with the help of several metrics among which detection accuracy and loss are the primary metrics. The software implementation of the developed system shows that the automatic parking lot detection system achieved high detection accuracy and real-time performance. © 2023 IEEE.

关键词： Classification (of information)

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Reverse blockchain technology and its application to improve the quality of control systems in conditions of network instability 6

Reverse blockchain technology and its application to improve...

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6th international conference on Blockchain computing and Applications, BCCA 2024

作者： Khilenko, Volodymyr Zinchenko, Andrii Stepanov, Oleksii Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava Slovakia Department of Information Technologies National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Institute of Transport Systems and Technologies National Academy of Sciences of Ukraine Dnipro Ukraine

ISBN: (纸本)9798350351538

The work proposes a technology for using a blockchain database to improve the quality of control systems for dynamic objects and technological processes in situations where the stability of communication systems is not ensured or loss of information and control signals is possible. The developed technology (hereinafter referred to as "reverse blockchain technology") involves the calculation of management signals based on a comparison of data from the blockchain database and forecast results obtained at the time of loss of the information signal. The description of the dynamics of the control object is considered in matrix form. Model experiments were carried out to select computational methods for processing signals and forming forecasts for the dynamics of an object. Recommendations are formulated to improve the reliability of control systems in conditions of instability of communication channels. © 2024 IEEE.

关键词： blockchain computational methods forecasting the dynamics of an object improving the quality of control systems for dynamic objects and technological processes instability of communication channels loss of information and control signals reverse blockchain technology

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Enhancing Spectrum Sensing in Cognitive Radio Networks using Deep Learning Models: A Solution for Low-SNR Challenges 3

Enhancing Spectrum Sensing in Cognitive Radio Networks using...

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3rd international conference on automation, computing and Renewable Systems, ICACRS 2024

作者： Ramakrishnan, P. Priyanga, V. Sathiya, A. Sharmiladevi, D. Samundeeswari, A. Annapoorani, B.T. Department of Electronics and Communication Engineering M.Kumaarsamy College of Engineering Tamilnadu Karur639113 India Department of Computer science Engineering Shreenivasa Engineering College Tamilnadu Dharmapuri635301 India M.Kumaarsamy College of Engineering Tamilnadu Karur639113 India Department of Electronics and Communication Engineering Sona College of Technology Tamilnadu Salem636005 India Department of Electronics and Communication Engineering Paavai Engineering College Namakkal637018 India Department of Electronics and Communication Engineering Kongu Engineering College Tamilnadu Erode638052 India

ISBN: (纸本)9798331532420

Spectrum sensing is a fundamental component of cognitive radio networks (CRNs), enabling efficient spectrum utilization by detecting and leveraging underutilized frequency bands. However, low Signal-to-Noise Ratio (SNR) environments present substantial challenges to reliable spectrum detection, leading to increased false alarms and missed detections. In this paper, we propose an enhanced deep learning approach utilizing advanced neural networks, including Generative Adversarial Networks (GANs), to improve spectrum sensing in low-SNR regimes. By generating high-fidelity, synthetic training data across varying SNR levels, our model gains robustness against noise interference, enabling superior feature extraction and detection accuracy. Comparative evaluations with other deep learning models demonstrate that the GAN-augmented spectrum sensing framework achieves significant performance improvements in low-SNR conditions, offering a promising solution to enhance spectrum reliability in CRNs. This work underscores the potential of GAN-based data augmentation and denoising in addressing noise-related challenges in spectrum sensing, with implications for the next generation of intelligent, adaptive CRN architectures. © 2024 IEEE.

关键词： Signal to noise ratio

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An IoT-Enabled Smart Helmet for Health Monitoring and Location Tracking of Miners 7

An IoT-Enabled Smart Helmet for Health Monitoring and Locati...

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2023 7th international conference On computing, communication, control And automation, ICCUBEA 2023

作者： Sagare, Sakshi Patil, Sakshi Choudhari, Srushti Sontakke, Prakash Bhattacharjee, Taniya Pimpri Chinchwad College of Engineering Department of Electronics and Telecommunication Pune India Pimpri Chinchwad College of Engineering Department of Electronics and Telecommunication Engineering Pune India Cmr Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350304268

In the mining industry, different complex operations create varying risk factors that allow a multitude of risk factors to appear. The Smart Helmet has been developed to provide safety to miners who work in an environment with many risk factors. In the mining area, Miners come into contact with hazardous gases which cause various respiratory diseases, lungs infection, and suffocation, sometimes resulting in the death of miners. Existing helmets help to detect and alert miners about abnormal conditions using buzzers. The proposed system helps to monitor the health of mining workers. Also, it detects hazardous gases using gas sensors. When hazardous gases cross the threshold value, oxygen is supplied to miners. The base station can track the location of miners and all the collected data from sensors is sent to the cloud using IoT. Real-time data is monitored at the base station (Mobile application/Workstation). According to the parameters, needful help will be provided to mining workers. Also, A message alert and emergency call will be sent to the registered contact information during an emergency. Thus, the proposed system helps the mining industry to reduce the number of accidents Whenever Hazardous gases exceed their threshold values, a buzzer gets activated to warn nearby workers. There is also a provision for a manual panic switch. In case of an unavoidable situation, workers will use it so that others can get alerted. Thus, the proposed system helps the mining industry to reduce the number of accidents. © 2023 IEEE.

关键词： Hazardous Gas detection Mining Industry Mobile Applications Node MCU 8266 Wireless communication

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Enhanced Accuracy in Underwater communication Prediction Utilizing LSTM Over RNN for Improved Efficiency 2

Enhanced Accuracy in Underwater Communication Prediction Uti...

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2nd international conference on automation, computing and Renewable Systems, ICACRS 2023

作者： Sandhiya, V. Sandhiya, J. Hema Malini, A. Archana, T. Department of ECE Saveetha Engineering College Chennai India

ISBN: (纸本)9798350340235

Underwater communication systems experience obstacles like confined bandwidth, elevated energy consumption, propagation lags, end-to-end delay (E-ED), complex media accessibility control, resource allocation, and power constraints. These challenges are resolved using energy-efficient protocols, which fall into two categories: constraint-basedand constraint-free. The study conducts a comprehensive assessment of constraint-free protocols in our project, considering environmental impact, data transmission rates, efficiency, energy consumption, E-ED, and propagation delay. This analysis provides insights into the pros and cons of these protocols, guiding future research in Underwater Wireless Sensor Networks (UWSNs). The study also presents routing strategies and their strengths and weaknesses, identifying areas for further investigation. In this research work, algorithms such as Long-short Term memory algorithm (LSTM), Recurrent Neural Network (RNN), Convolution Neural Network (CNN), and Artificial Neural Network (ANN), are employed and analyzed in terms of accuracy. The results demonstrate that Long-short Term memory algorithm (LSTM) outperforms better than other existing algorithms in terms of accuracy. © 2023 IEEE.

关键词： Data Transmission Rate End to End delay (E-ED) Long-short term memory algorithm (LSTM) Recurrent Neural Network (RNN) Underwater wireless sensor network (UWSN)

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